Agboola Victor

CHAPTER ONE 1.0INTRODUCTION 1.1Background of the Study Sleep is a universally conserved process within the animal kingdom (Bishir et al., 2020). Indeed, sleep is not a passive state, but a heavily active process that plays a role in clearance of build up protein, and upregulation of many anabolic hormones, such as growth hormone and prolactin (Zielinski et al., 2016). There is mounting evidence that many cortical and subcortical areas, including the hippocampus, cerebellum, and medial prefrontal cortex, regulate sleep (Spencer et al., 2017). Furthermore, it has been demonstrated that hippocampal memory consolidation takes place during sleep, particularly during slow wave and rapid eye movement (REM) sleep (Klinzing et al., 2016). New memories are redistributed into the neocortex (Rasch et al., 2006) by minimal cholinergic neurons, which prevent hippocampus hypoactivity. It has been demonstrated that sleep deprivation (SD) impairs spatial working memory (Ward et al., 2017), affects long-term memory retention (Krishnan et al., 2016), and significantly impairs the capacity to distinguish between safety and fear-relevant cues (Ghasemi et al., 2022). According to certain theories, the hippocampus is especially susceptible to even 5–6 hours of total SD, which could further reduce the density of the spine in the CA1 subregion but not in the CA3 subregion (Havekes et al., 2016). Chronic short-term sleep restriction has also been shown to decrease CA1 glial activity, as well as CA1 neuron numbers and volume (Owen et al., 2021). It has been observed that sleep deprivation inhibits the growth of granular cells in the hippocampus's CA1 pyramidal neurons and Dentate Gyrus (DG) (Mueller et al., 2015). However, studies have shown that sleep deprivation reduces synaptic density and impairs long-term potentiation (LTP), which is essential for learning and memory (Havekes et al., 2016). It increases levels of pro-inflammatory cytokines (such as TNF-α and IL-6) and oxidative stress markers in the PFC, contributing to neuronal damage (Wadhwa et al., 2018). on the other hand, sleep deprivation has also been shown to impair motor coordination tasks, such as the rotarod test, indicating cerebellar dysfunction and which also alters GABAergic signaling in the cerebellum, leading to impaired motor function (Zhu et al., 2018; Silva et al., 2020). Methamphetamine (METH) is a psychostimulant that is abused worldwide (UNODC, 2018; Yang et al., 2018). METH is a member of the amphetamine-type stimulants (ATSs) that include amphetamine, methylene dioxy methamphetamine (MDMA) and other designer amphetamine (Chomchai and Chomchai, 2015; Yang et al., 2018). METH reduces dopamine (DA) and serotonin (5-HT) levels in the PFC, which impairs cognitive function and impulse control (Davidson et al., 2001). Studies have shown that METH-induced neurotoxicity damages dopaminergic terminals and decreases tyrosine hydroxylase (TH), an enzyme essential for dopamine synthesis (Cadet et al., 2003). The depletion of serotonin further exacerbates mood disturbances and cognitive deficits, leading to behavioral impairments (Krasnova & Cadet, 2009). METH significantly disrupts synaptic plasticity in the hippocampus, particularly by impairing long-term potentiation (LTP), a mechanism crucial for learning and memory (Belcher et al., 2005). Studies indicate that METH reduces the expression of brain-derived neurotrophic factor (BDNF), a key regulator of synaptic plasticity, leading to weakened synaptic connectivity and memory deficits (Williams et al., 2013). Behaviorally, METH-exposed mice perform poorly in the Morris water maze and novel object recognition tasks, confirming impairments in spatial and recognition memory due to hippocampal dysfunction (Deng et al., 2017). However, Purkinje cells, the primary output neurons of the cerebellar cortex, are particularly vulnerable to METH-induced neurotoxicity. Studies show that METH exposure leads to Purkinje cell loss and shrinkage, impairing cerebellar function (Camarasa et al., 2010). Additionally, METH damages cerebellar granule cells, reducing neuronal density and synaptic connectivity, which contributes to motor dysfunction (Granado et al., 2013). METH induces oxidative stress in the cerebellum by increasing reactive oxygen species (ROS) and reactive nitrogen species (RNS), leading to lipid peroxidation, mitochondrial dysfunction, and neuronal apoptosis (Herring et al., 2008). This oxidative imbalance impairs cerebellar signaling and contributes to neuronal degeneration (Jayanthi et al., 2005). Moreover, METH activates cerebellar microglia and astrocytes, releasing pro-inflammatory cytokines (TNF-α, IL-1β, IL-6), which further exacerbate cerebellar damage (Zhu et al., 2006). Furthermore, METH exposure disrupts cerebellar glutamatergic and dopaminergic signaling, which affects motor learning and coordination (Williams et al., 2013). Therefore, this study delve to compare the effect of sleep deprivation and exposure METH on the PFC, Hippocampus and cerebellum of mice. 1.2Statement of the Problem Sleep deprivation and methamphetamine (METH) abuse are both known to significantly impact brain function, particularly in regions associated with cognition, memory, and motor control. The prefrontal cortex (PFC), hippocampus, and cerebellum play critical roles in executive function, learning, and coordination, respectively. However, while both sleep deprivation and METH exposure can independently cause neurodegeneration, synaptic alterations, and cognitive impairment, their comparative effects on these brain regions remain unclear. 1.3Aims of the Study Aims is to compare the effect of sleep deprivation and exposure to crystal methamphetamine on the PFC, hippocampus and cerebellum of mice. 1.4Objective of the Study To investigate the comparative effects of sleep deprivation and METH exposure on neuronal integrity, synaptic plasticity, and behavioral outcomes in mice. By analyzing histological, biochemical, and behavioral changes in the PFC, hippocampus, and cerebellum. 1.5Specific Objectives Of Study The specific objectives of the study are to: • To examine the effects of sleep deprivation and methamphetamine exposure on the structural integrity of the prefrontal cortex (PFC), hippocampus, and cerebellum in mice. • To assess the impact of sleep deprivation and methamphetamine exposure on cognitive functions, including learning and memory, using behavioral tests. • To evaluate changes in neuronal density and synaptic plasticity markers in the PFC, hippocampus, and cerebellum following sleep deprivation and methamphetamine exposure. • To analyze the levels of oxidative stress and neuroinflammation in the PFC, hippocampus, and cerebellum as a result of sleep deprivation and methamphetamine exposure. • To compare the severity and nature of neurotoxicity induced by sleep deprivation and methamphetamine exposure in the targeted brain regions. • To investigate potential interactions or synergistic effects between sleep deprivation and methamphetamine exposure on brain function and behavior. 1.6Scope of the Study This study focuses on investigating the comparative effects of sleep deprivation and methamphetamine (METH) exposure on the prefrontal cortex (PFC), hippocampus, and cerebellum of mice. The research will be conducted using a controlled experimental design, where male and female laboratory mice will be subjected to either sleep deprivation, METH administration, or a combination of both. 1.7Significance Of The Study Findings from this study contribute to a better understanding of how sleep deprivation and METH exposure independently or synergistically affect brain function and behavior. 1.8Limitation Of The Study The study is limited to short-term effects, focusing on acute and sub-chronic exposure periods. It does not include long-term recovery mechanisms or the potential influence of genetic and environmental factors beyond the experimental conditions. CHAPTER TWO 2.0Literature Review 2.1Sleep Deprivation 2.1.1Sleep Sleep Stages Sleep in humans, as well as in other mammals, can be divided into two main states: rapid eye movement (REM) sleep and non-rapid-eye movement (NREM) sleep. Whereas REM sleep consists of only one stage, NREM sleep consist of three stages (N1, N2, and N3), with each stage representing increased depth of sleep (Iber et al., 2007). In the human sleep cycle, REM and NREM sleep alternate in an ultradian rhythm every 90 minutes. Although the length of the REMNREM cycle remains mostly stable during the sleeping period (i.e., night), the ratio of REM to NREM sleep changes every cycle. This means that NREM sleep, specifically N3 stage, dominates in the early night period, whereas REM sleep dominates in the late night (or early morning) period. However, the reasons for the organisation of sleep stages (late stages of NREM in the early sleeping phase, and REM with early NREM stage in the late sleeping phase) remain undiscovered (Walker & van der Helm, 2009). Adults sleep approximately 75% to 80% in NREM stages, and about 20% to 25% in REM stage. In NREM sleep, N1 is approximately 2% to 5% of sleep, whereas N2 is approximately 45% to 55% of sleep, and N3 is approximately 13% to 23% of sleep. Adults begin in NREM sleep, which progresses from N1 to N2, and lastly N3. The first REM stage occurs approximately 80 to 100 minutes later, and thereafter the episodes of REM appear approximately 90 minutes after NREM sleep (Carskadon & Dement, 2011). REM sleep is characterized by muscle atonia, tonic/phasic events, and episodes of rapideye movements. A phasic event of REM sleep is depicted on the electroencephalography (EEG) when rapid-eye movements occur, whereas the tonic events of REM sleep illustrate the intervals between the rapid-eye movements. It is also during REM sleep when the brain is most activated and when dreaming is typical, whereas the body is paralyzed. In contrast, NREM sleep is characterized by a less active brain in a movable body. In NREM sleep, there are three different waveforms that are visible on the electroencephalogram: sleep spindles, K-complexes, and highvoltage slow waves. NREM stages have the lowest arousal thresholds during the first stage (N1). The highest arousal thresholds occur during the third stage (N3), a stage also referred to as slow wave sleep (SWS) (Carskadon & Dement, 2011). 2.1.2Functional Neuroanatomy of Sleep All sleep stages in REM and NREM are related to dramatic changes in functional brain activity. In particular, neuroimaging studies have revealed that during REM sleep there is increased activity in cortical and subcortical brain regions associated with emotions, including the insula, medial prefrontal cortex (MPFC), amygdala, hippocampus, and striatum (Dang-Vu et al., 2010; Goldstein & Walker, 2014; Miyauchi et al., 2009). Other brain areas have also been reported to be activated during REM sleep, including the occipital cortex, pontine tegmentum, thalamic nuclei, and mediobasal prefrontal lobes. However, during NREM and in particular SWS, temporal lobe, thalamus, brain stem, basal ganglia, and prefrontal areas a significantly decreased activity. In comparison, during REM sleep, decreased activity has been found in the parietal cortex, posterior cingulate cortex, and dorsolateral prefrontal cortex (DLPFC) (Walker & van der Helm, 2009). 2.1.3Sleep and Emotional Processing 2.1.3.1The Role of Sleep in Emotional Memory Processing Several studies have investigated the role of sleep in regards to emotional memory processing, and in particular emotional memory encoding and emotional memory consolidation. In experimental literature, studies on the impact of sleep on memory have been represented in two stages: before learning (encoding memory) and after learning (memory consolidation). Before learning, describes the initial formation (encoding) of new information, whereas after learning, describes the long-term solidification (consolidation) of new memories (Walker & van der Helm, 2009). Sufficient sleep before learning has been shown to benefit memory encoding of episodic information (Van der Werf et al., 2009). In contrast, sleep deprivation before learning has shown to impair the ability for successful episodic memory encoding (Yoo et al., 2007). Furthermore, research has also investigated the effects of sleep on emotional memory encoding. In a study conducted by Kaida and colleagues (2015), it was found that total sleep deprivation (i.e., avoidance of sleep for at least one night) impaired the ability to encode emotional information, whereas REM sleep deprivation (i.e., avoidance of REM sleep) did not affect emotional memory encoding. Memory consolidation appears to be greatly affected by emotions. Although neutral memories become more difficult to remember over time (Frankland & Bontempi, 2005), emotional memories seem to be easier to remember (LaBar & Cabeza, 2006). Evidence has also emerged that indicates the important role of sleep in long-term consolidation of emotional memories (Holland & Lewis, 2007). For instance, Wagner and colleagues (2006) demonstrated that emotional memories after four years were better remembered by sleep-rested subjects compared to sleep-deprived subjects. 2.1.4.Sleep Deprivation and Emotional Reactivity: Behavioural Studies There are different theories about the function of sleep on emotional processing. This review will focus on emotional reactivity, and more specifically how it is affected by sleep deprivation. According to behavioural studies, partial sleep deprivation (i.e., less sleep than normal) has been linked with increased emotional disturbance following one week of restricted sleep (Dinges et al., 1997). Moreover, the effects of partial sleep deprivation have revealed an increase of negative emotions in experiencing adverse events, and dampened increase of positive emotions in experiencing pleasant events (Zohar et al., 2005). In terms of one night of total sleep deprivation, it has been linked with greater subjective reports of anxiety, anger, and stress in response to mildly stressful situations (Minkel et al., 2012). It has also been reported that one night of sleep loss leads to increased impulsivity to negative stimuli (Anderson & Platten, 2011). One night of sleep loss has also shown to affect emotional evaluation. Tempesta and colleagues (2010) reported that subjects perceived neutral pictures as more negative after sleep deprivation. In contrast, no significant effects of sleep deprivation were found in regards to positive and negative stimuli (Anderson & Platten, 2011). One night of sleep loss has also shown to affect emotional evaluation. Tempesta and colleagues (2010) reported that subjects perceived neutral pictures as more negative after sleep deprivation. In contrast, no significant effects of sleep deprivation were found in regards to positive and negative stimuli. 2.2METHAMPHETAMINE Methamphetamine has been labeled as “America’s most dangerous drug” and has received significant public health attention. The substance was first synthesized at the University of Berlin in 1887, and soldiers utilized the drug during World War II for stimulation and appetite suppression (Gonzales et al., 2010). Methamphetamine received campaign recognition and inclusion in the Comprehensive Drug Abuse Prevention and Control Act of 1970. Stimulant addiction and tolerance are heavily documented in the literature; increasingly larger doses maintain euphoria in short time periods to withstand stimulant tolerance.1 Between 2013 and 2019, deaths related to methamphetamine use quadrupled from 3,616 to 16,127. 2 The deaths commonly had opioid involvement, as synthetic opioids are mixed with methamphetamine to deliver balanced euphoria (Han et al., 2021). Methamphetamine deaths are highest in the Western United States compared to the Eastern United States and affect all age groups, with shifting paradigms of younger populations being affected (Jones et al., 2020). Ready availability and low cost coupled with immediate onset of action are drivers of methamphetamine’s popularity. Methamphetamine induces multiple symptoms and side effects. Cardiopulmonary symptoms including chest pain, palpitations, and shortness of breath are common, and methamphetamine-related myocardial infarction may correlate with thrombus formation by direct and indirect mechanisms (Han et al., 2017). Cardiomyopathy has also been reported with methamphetamine use. Central nervous system (CNS) symptoms include agitation, anxiety, delusions, hallucinations, and seizures (Han et al., 2017). Methamphetamine-induced psychosis may unmask underlying psychiatric disorders (Han et al., 2017). It can also cause cerebral vasculitis, which elicits cortical blindness and ischemic strokes. Endocarditis, HIV, and viral hepatitis are associated with methamphetamine use primarily through the intravenous use of the drug, and pulmonary hypertension has been reported in methamphetamine use (Han et al., 2017). Systemic complications include hyperthermia, rhabdomyolysis, and acute liver and/or renal failure (Han et al., 2017). Approximately 24 million people use methamphetamine worldwide. However, less than one-third of methamphetamine users receive treatment, and rural areas are disproportionately affected by methamphetamine use (Kipke et al., 1995). There is little known regarding the medical treatment of methamphetamine addiction at this time. Using intravenous amphetamines causes high rates of infectious disease in rural communities (Kipke et al., 1995). 2.2.1Epidemiology of METH Stimulants, including methamphetamine, cocaine, ecstasy, and prescription stimulants, are the second most used substances in the United States (Smid et al., 2019). Stimulant use disorder (SUD) is prevalent in the United States, Australia, South-East, and South-West Asia, with more than 35 million people worldwide using methamphetamine (Blum et al., 2021). Methamphetamine use increased fourfold from 2015 to 2016 (Paulus and Stewart, 2020). Due to this increase in methamphetamine use and its associated medical complications, the mortality rate associated with methamphetamine use has doubled over the past ten years (Paulus and Stewart, 2020). People who use methamphetamine include individuals of all genders, adolescents of high school age, young professionals, and older adults (Blum et al., 2021). Women who use methamphetamine have an observed death rate 26 times that of women who do not use methamphetamine (Smid et al., 2019). Studies have found that younger users are more likely to take methamphetamine for recreational purposes and performance enhancement, while older individuals use methamphetamine to gain relief from stressful life events (Paulus and Stewart, 2020). 2.2.2Neurophysiology Of Methamphetamine Use Crystal methamphetamine is the most commonly used form, and it is predominantly either smoked or injected (Blum et al., 2021). When methamphetamine users ingest, inhale, or inject the drug, it enters the bloodstream, rapidly crosses the bloodbrain barrier, and penetrates the brain due to its lipophilic nature (Vocci and Appel, 2007; Shin et al., 2017) The half-life of methamphetamine depends on how it is absorbed but usually ranges from five to thirty hours (Blum et al., 2021). Methamphetamine causes this excess release by emptying synaptic vesicles within the cytosol, blocking the transport of endogenous neurotransmitters, inhibiting synaptic reuptake, and decreasing the expression of transporters at the cell surface (Vocci and Appel, 2007; Shin et al., 2017). When dopaminergic neurons take up methamphetamine, it inhibits the type 2 vesicular monoamine transporter (VMAT2), an intracellular transporter on the surface of synaptic vesicles responsible for taking up dopamine (Vocci and Appel, 2007; Shin et al., 2017). Similar to the inhibition of VMAT2, methamphetamine also reverses the transmembrane dopamine transporter (DAT) (Vocci and Appel, 2007; Shin et al., 2017). Amphetamines promote phosphorylation of the N-terminal of DAT and VMAT2 by protein kinase C or calcium-calmodulin dependent kinase, which leads to their inhibition (Shin et al., 2017). Methamphetamine also activates presynaptic scaffolding proteins via protein kinase C, increasing the internalization of DAT (Shin et al., 2017). The inhibition of VMAT2 and DAT transporters leads to a surplus of dopamine released directly into the mesolimbic, neocortical, and nigrostriatal pathways.6–8 This excess dopamine release activates the brain’s reward system and creates a sense of euphoria, enhanced mental acuity, positive mood, and social and sexual disinhibition, leading to substance addiction (Vocci and Appel, 2007; Shin et al., 2017). This supraphysiologic dopamine release leads to neuronal changes in the reward system that lead to tolerance and drug-seeking behavior. 2.2.3Adverse Effects of Methamphetamine Women with SUD have high rates of pregnancy complications due to the socioeconomic uses associated with methamphetamine use and the adverse effects of the use itself. The two highest complications of methamphetamine use in pregnancy were placental abruption, high rates of operative deliveries, and preterm birth (Thompson et al., 2004). The use of concomitant substances leads to increased mortality (Thompson et al., 2004). Gorman et al. reported high rates of hypertensive disorders of pregnancy, and intrauterine fetal demise and neonatal demise were elevated (Gorman et al., 2014) Methamphetamine is a strong vasoconstrictor (Kevil et al., 2019). This vasoconstriction is a probable explanation for the high rates of hypertensive disorders. Vasoconstriction can also be an explanation for the increased rates of fetal demise. Psychiatric manifestations are also common in methamphetamine use. Psychiatric symptoms may include agitation, anxiety, delusions, and psychosis (Zweben et al, 2004). Additionally, methamphetamine use correlates with higher underlying psychiatric disorders and health services use (Glasner-Edwards et al., 2008). There are also multiple functional, molecular, and structural neuroimaging changes in those who use methamphetamine. The majority of these changes are located in cortical and striatal pathways (Glasner-Edwards et al., 2008). These pathways contribute to cognitive and behavioral changes promoting compulsive drug use. Methamphetamine use also correlates with smaller cortical gray matter volume than larger striatal gray matter volume (Schuckit, 2006). Deficits in gray matter volume are seen in several areas, including the anterior cingulate cortex, dorsolateral prefrontal cortex, orbitofrontal cortex, superior temporal cortex, and hippocampus (Thompson et al., 2004). Cortical gray matter deficiencies may eventually reverse after cessation of methamphetamine use (Berman et al., 2008). Further, white matter volume abnormalities are also linked to methamphetamine use (Morales et al., 2012). Those who use methamphetamine have lower amounts of diffusion across several brain areas, including prefrontal white matter, corpus callosum, superior corona radiata, and the perforant path (Tobias et al., 2010). Hypertrophy from methamphetamine use followed by abstinence may lead to altered gliosis and myelination (Tobias et al., 2010). Effects on memory and cognition are another common adverse effect. Many students, both college and medical, may believe that the use of prescription stimulants may improve their academic performance. However, in a recent narrative review, it was highlighted that the actual effect on academic performance wasn’t an improvement in academic performance at all, as evidenced by grade point averages, and there is a possible decrease in executive function in students who misuse stimulant medications than those without misuse (Edinoff et al., 2022). The misuse of stimulant medications may not seem like an issue when we view it as a medication. However, it is important to note that the illicit methamphetamine used illicitly is a metabolite of amphetamine, found in stimulant medications such as Adderall (Edinoff et al., 2022). When methamphetamine is used illicitly, larger amounts are used. However, the long-term effects of the misuse of the smaller medication doses of amphetamine are not well known at the time. Long-term methamphetamine use in animal models demonstrated cardiac myocyte atrophy and necrosis, and the effects were partially reversible with abstinence (He et al., 1996). Methamphetamine leads to hypertension due to potent vasoconstriction and hypertensive cardiomyopathy (Darke et al., 2008). Case series have demonstrated associated left ventricular dysfunction and ejection fraction reduction with chronic use (Wijetunga et al., 2003). Methamphetamine use may prolong the QTc interval leading to various arrhythmias (Richards et al., 2020). Myocardial infarction and methamphetamine use are correlated with spasms of coronary vessels and coronary stenosis (Wijetunga et al., 2003). Most strokes in amphetamine users are hemorrhagic, and the increase in hemorrhagic strokes as compared to the general population may be linked to arterial hypertension provoked by methamphetamine (Richards et al., 2020). Rhabdomyolysis is also correlated with methamphetamine use (Richards et al., 2020). Symptoms associated with rhabdomyolysis include elevated troponin, blood urea nitrogen, creatinine concentration, and male gender (Richards et al., 2020). Rhabdomyolysis should be considered in patients with histories concerning methamphetamine use (Richards et al., 2020). The complications of rhabdomyolysis can be liver damage and renal failure. 2.2.4METHAMPHETAMINE TOXICITY 2.2.4.1Mechanisms of Methamphetamine Neurotoxicity Methamphetamine misuse can cause various neurological complications resulting in significant morbidity and mortality (Blum et al., 2021) Methamphetamine can significantly alter microglial neuroimmune function, elicit neuroinflammation, and cause dopaminergic neurotoxicity (Blum et al., 2021). Methamphetamine-induced neurotoxicity depends on multiple mechanisms, including increased dopamine conversion into reactive oxygen species (ROS), ubiquitin-proteosome system dysfunction, increased p53 expression which increases inflammatory cytokines leading to altering DNA repair, and disrupting the blood-brain barrier (Paulus et al., 2020). The conversion of the excess dopamine into ROS is one of the leading theories behind methamphetamine-induced neurotoxicity (Vocci et al., 2007). Methamphetamine is associated with increased astrocytosis and microglial activation, producing ROS (Cadet et al., 2009) Uncontrollable ROS within the neuron damages its integrity (Vocci et al., 2007). This phenomenon occurs because positively charged methamphetamine alters the mitochondria by disrupting the transmembrane potential and pH gradient (Steinkellner et al., 2011). This inhibits the electron transport chain shifting the brain from an oxidative state into a glycolytic metabolic state (Paulus et al., 2020). This leaves the brain with less efficient energy, an acidic microenvironment, and altered cell signaling similar to the brain state involved in neurodegenerative CNS diseases (Paulus et al., 2020). Further contributing to neurotoxicity, methamphetamine upregulates pro-apoptotic proteins such as Bax, Bad, and Bid and downregulates anti-apoptotic proteins such as Bcl-2 and Bcl-X. This leads to the release of cytochrome C from the mitochondria and subsequent neuronal death (shin et al., 2017). Methamphetamine also increased the expression of p53, a tumor suppressor gene (shin et al., 2017). P53 can increase the transcription of pro-apoptotic factors or enter the mitochondria and facilitate apoptosis by releasing cytochrome C (shin et al., 2017). Further more, methamphetamine use contributes to neuroinflammation by inducing microglial activation via increased expression of pro-inflammatory cytokines such as TNFa, interleukin-1B, and interleukin-6 (shin et al., 2017). The neurotoxicity of methamphetamine use may be directly related to some of the symptoms and behavior patterns exhibited by those who chronically use it. Through neuroimaging post-mortem brains, methamphetamine use is associated with widespread gray and white matter alterations in the frontostriatal system, the left temporal gyrus, and the right inferior parietal lobe ((Paulus et al., 2020). With these alterations, the damaged orbitofrontal cortex-dorsomedial striata may be telling the methamphetamine user to “go,” yet the damaged dorsolateral frontal striatal cannot balance that stimulus with “stop” (Paulus et al., 2020).This imbalance can affect decision-making and inhibitory processing during the recovery stage (Paulus et al., 2020). Methamphetamine-induced neurotoxicity in serotonergic systems is more diffuse, involving the striatum, hippocampus, septum, amygdala, and hypothalamus leading to mood changes, psychosis, and memory impairment (Paulus et al., 2020). Figure 2.1: Showing Mechanism of Action of Methamphetamine Neurotoxicity (Jayanthi et al., 2021) 2.2.5COMPLICATIONS OF METHAMPHETAMINE USE 2.2.5.1Cardiovascular Pathologies Methamphetamine users are at an elevated risk of cardiac pathology. Emergency department data has shown consistent chest pain, cardiac arrhythmias (tachycardia), palpitations and hypertension to be among the most common physical symptoms after methamphetamine intoxication (Turnispeed et al., 2003). A prolongation of the QTc beyond 440 ms is reported among 27.2% of the methamphetamine users (Haning and Goebert, 2007). Cardiovascular consequences of methamphetamine use include acute coronary syndrome, acute myocardial infarction, acute aortic dissection, and sudden cardiac death (Kaye et al., 2007). Cardiovascular complications associated with methamphetamine use can occur with all of the major routes of administration (Kaye et al., 2007). When methamphetamine is combined with alcohol, cocaine oropioids, toxicity and stress on the cardiovascular system is increased (McGregor et al., 2008). 2.2.5.2Cerebrovascular Complications Methamphetamine use is associated with ischemic stroke, intracerebral haemorrhage and subarachnoid haemorrhage, especially among young patients (Ho et al., 2009). A study showed no evidence that the ischemic stroke associated with methamphetamine use is due to an inflammatory etiology but may be due to a process of accelerated atherosclerosis (Ho et al., 2009). Methamphetamine leads to increased catecholamine levels, leading to coronary vasoconstriction, production of oxygen-free radicals, myocardial fibrosis, and cardiomyopathie because of the direct toxicity to extra and intracerebral vessels, leading to changes in luminal calibre (Ho et al., 2009). 2.2.5.3Neurotoxicity Repeated use of methamphetamine involves the degeneration of dopamine and serotonin axons and termini, located in the fronstostrial region, leading to depletion of these monoamines (Cadet et al., 2005;Clemens et al., 2007). The mechanisms of neurotoxicity are not understood completely, but involve oxidative stress and apoptosis (Kita et al., 2003; Itzhak and Achat-Mendes, 2004). Primate experiments demonstrate that methamphetamine use can lead to neurotoxicity human positron emission tomography and magnetic resonance imaging showed brain abnormalities including inflammation (Petit et al., 2012), reduced neuronal density (Petit et al., 2012) and reduced density of dopaminergic markers (Petit et al., 2012). These abnormalities mediate cognitive deficits among methamphetamine users, caused by damages in the cingulated, frontal, and striatal regions (Petit et al.,-Parkinson’s Disease Parkinson’s disease psychomotor disturbances have been reported among methamphetamine heavy users (Caligiuri and Buitenhuys, 2005). To confirm this hypothesis, a retrospective case-controlled study revealed that prolonged use of methamphetamine is associated with an eight-fold increased risk of Parkinson’s disease with an average of 27 years between amphetamine exposure and the onset of signs (Garwood et al., 2006). 2.2.5.5Neuropsychological Impairment Neurocognitive impairment caused by methamphetamine is caused by frontostrial and limbic abnormalities. The main functions altered are learning, episodic memory, executive functions, speed of information treatment, working memory and perceptual narrowing (Scott et al., 2007). The cognitive deficits persist over six months after withdrawal (Clark et al., 2006; Kim et al., 2006). 2.2.5.6Sexual Behaviours Methamphetamine use is reported to enhance sexual pleasure, to facilitate prolonged sexual activity, and to delay and increase orgasm (Semple et al., 2004; Shoptaw and Reback, 2007). Methamphetamine is used in combination with drugs such as sidenafil to enhance sexual performance (Bang-Ping, 2009). The association with others drugs (cocaine, rohypnol) promotes compulsive sexual activity and high-risk activities such as unprotected, anonymous and receptive anal sex among homosexual methamphetamine dependent users (Bang-Ping, 2009). 2.2.5.7Other Outcomes Studies showed a strong association between methamphetamine use and dental diseases, with a greater number of decayed, missing or extracted teeth among methamphetamine users compared to controls (Hamamoto and Rhodus, 2009). Others studies reported that teeth grinding (McGrath and Chan, 2005) and jaw pain (McGrath and Chan, 2005) and “meth mouth“[DDDDD] were more common among the group of methamphetamine users. “Meth mouth” is a term used to describe the mouth of a methamphetamine user because of the rampant tooth decay that often occurs with the use of this dangerous drug (Heng et al., 2008). Using meth can cause decay so badly that the teeth cannot be saved and must be pulled out instead. Several mechanisms have been proposed (Methamphetamine-induced xerostomia, increased consumption of soft drinks, reduced behaviours) (Petit et al., 2012), although it is noteworthy that all causal pathways remain hypothetical (Klasser and Epstein, 2006). 2.2.6Pharmacological Approaches Recent improvements in the understanding of the underlying neurobiology of methamphetamine dependence have led to the emergence of promising targets. The adopted strategy has to a large extent resembled the approach to research on cocaine dependence pharmacotherapy, and employed similar preclinical and clinical models [SS]. No substantial evidence for efficient treatment has yet emerged (Karila et al., 2010). Clinical trials using aripiprazole (Stoops et al., 2006; Stoops, 2006], GABA agents (gabapentin (Heinzerling et al., 2006; Urschel et al., 2007), baclofen (Heinzerling et al., 2006), vigabatrin (Fechtner et al., 2006; Brodie et al., 2005), SSRIs (Petit et al., 2012), ondansetron (Dremencov et al., 2006; Johnson et al., 2008) and mirtazapine (Harper and Napler, 2005) have failed to show efficacy ( Karila et al., 2010). In a double –blind, placebo-controlled design, naltrexon 50 significantly decreased the subjective effects produced by drugs in dependent patients. Trials involving bupropion (Elkashef et al., 2008; Shoptaw et al., 2008) and modafinil (McElhiney et al., 2009; McGregor et al., 2008) have demonstrated possible benefits in treating methamphetamine use in dependent patients. The PROMETA protocol, consisting of flumazenil, gabapentin and hydroxyzine, was tested to treat methamphetamine dependence. It appears to be no more than a placebo in reducing methamphetamine use, retaining patients in treatment or reducing methamphetamine craving (Ling et al., 2011). Immunotherapies, an innovative treatment strategy of drug addiction, may be effective in blocking the effects of drug abuse (Meijler et al., 2004). Preclinical studies have shown the therapeutic potential of the anti-methamphetamine monoclonal antibodies (AMMA) approach (Danger et al., 2006; Gentry et al., 2006). Reduction of methamphetamine self-administration, locomotor activity and inhibition of discriminative stimulus effects of methamphetamine was shown in rats and pigeons (Byrnes-Blake et al., 2005). The two primary indications for the use of AMMA in the treatment of human methamphetamine dependence would be overdose and relapse prevention (Gentry et al., 2009). 2.2.7Studies in the Literature The toxicity and effects of methamphetamine have been widely studied, and multiple studies have been published looking at both the short- and long-term effects of methamphetamine. The kidney is one of the organs most commonly affected by methamphetamine use, and its effects are well documented. Isoardi et al. in 2020 performed a prospective observational series looking at patients with self-reported recent methamphetamine use or who had both a positive urine drug screen for methamphetamine and elevated creatinine. These patients then had urinary neutrophil gelatinase-associated lipocalin (NGAL), repeat serum creatinine, creatine kinase, and cystatin c concentrations measured to assess if the patient had developed an acute kidney injury (AKI). Of 595 patients presenting to the emergency department, 73 had elevated creatinine with a median concentration of 125 μmol/L, with 90% of the cases meeting diagnostic criteria for AKI. It was also noted that concurrent rhabdomyolysis was seen in 44% of cases with a median creatine kinase level of 2695, and NGAL levels were elevated in 10% of cases. None of the patients in this study required dialysis as a result of their AKI, and a majority of the patients had resolution of their AKI with crystalloid therapy and were discharged within 19 hours of admission (Isoardi et al., 2020). In 2021 Maheshwari and Athiraman published a case study involving a 27-year-old male who presented after ingesting four “M30” pills he had purchased from the internet. This patient’s urine drug screen was positive for methamphetamine, cannabinoid, fentanyl, and ethanol. He presented with acute confusion, hypotension, tachycardia, hyperthermia, and hypoxia. He was also found to have severely elevated transaminases, white blood cell count, cardiac enzymes, and CK levels. Due to this patient’s acute condition, he was admitted for observation and monitoring. He developed acute tubular necrosis (ATN) due to elevated CK levels and rhabdomyolysis and eventually required hemodialysis while in the hospital. At discharge, the patient required dialysis three times a week for fulminant kidney failure and was awaiting a kidney transplant due to irreversible kidney damage (Maheshwari and Athiraman, 2021). Long-term methamphetamine use is likely linked with chronic kidney disease. However, more studies are required to assess the degree to which they are linked (Maheshwari and Athiraman, 2021). Methamphetamine overdose commonly causes renal dysfunction and can cause multisystem organ failure and lead to death when taken in high enough doses. Pillai et al. wrote a case report in 2019 of a 27-year-old male who reportedly ingested approximately 1.5 grams of methamphetamine. On arrival at the ED, he was noted to have a Glasgow coma scale (GCS) score of 3T and was noted to have a heart rate of 200/min showing sinus tachycardia on cardiac monitoring, respirations at 40/min, and was hypotensive with systolic blood pressure in the 60s. His muscles were found to be rigid, and he had a core body temperature of 42.2 °C (108 °F). Initial laboratory assessment of the patient showed respiratory acidosis, hyperkalemia, AKI, and elevated CK levels. The patient’s urine drug screen was positive only for methamphetamine. Aggressive measures were taken to correct the patient’s acidosis, hypotension, and hyperthermia through an infusion of sodium bicarbonate, fluid resuscitation, and active cooling. The patient was transferred to the intensive care unit (ICU) after an improvement of core temp to 37 °C, and repeat arterial blood gas (ABG) showed increased blood pH. Four hours after transfer to the ICU, the patient began to cough up blood through his endotracheal (ET) tube, at which time labs drawn showed decreased platelet count, elevated prothrombin time, elevated international normalized ratio (INR), decreased fibrinogen, and elevated fibrin split products consistent with disseminated intravascular coagulation (DIC). After many hours of aggressive critical care through transfusion of several units of fresh frozen plasma and leukocyte-reduced red blood cells, the patient went into cardiac arrest with asystole and could not be resuscitated (Pillai et al., 2019). It is theorized that the prevention of rhabdomyolysis and AKI with aggressive fluid resuscitation and early detection of DIC in patients with methamphetamine overdose can improve outcomes (Pillai et al., 2019). The cardiovascular system effects of methamphetamine have been studied. Darke et al. conducted a study analyzing 894 autopsy reports of methamphetamine-related deaths looking at the effects of long-term methamphetamine use on the cardiovascular system. The mean age of patients was 37.9 years, with 78.5% being males. They found that a quarter of patients had enlarged hearts, and 18.9% of cases were diagnosed with left ventricular hypertrophy. It was also noted that 19.0% of cases had severe coronary artery disease at the time of death. The left coronary artery was the most commonly stenosed vessel, which was seen in 16.6% of cases. Signs of previous ischemic events evident by replacement fibrosis of the myocardium were seen in 19.8% of cases, and the criteria for diagnosis of cardiomyopathy was met in 5.5% of cases. A significant number of cases (32.7%) showed histological evidence of hypertension, which was seen with changes to all vascular layers, including fibrosis of the perivascular adipose tissue, particularly affecting the muscular arteries greater than other blood vessels. A trend was seen that a majority of patients whose cause of death was not attributed to cardiovascular disease were noted to still have clinically significant levels of cardiovascular disease in the form of cardiomegaly, left ventricular hypertrophy, severe coronary artery disease, replacement fibrosis, and cardiomyopathy. It was also shown that cardiovascular disease was more commonly seen in males, particularly those older than 35. Despite the overall young age of the patients seen in the study, the rates of cardiovascular disease were high and significantly elevated compared to the general population (Darke et al., 2017). Huang et al. also conducted a long-term study that followed 1,315 inpatients treated for methamphetamine use in Taiwan between January 1, 1997, and December 31, 2000. Patients were matched with a proxy comparison group, and patients were monitored for any complications until December 31, 2010. This study also saw a male patient population, approximately half younger than 30. The methamphetamine cohort had higher incidences of cardiovascular disease and stroke events when compared to the control cohort. They also noted an increased risk of cardiovascular disease and stroke complications, particularly arrhythmia and hemorrhagic stroke. The increased risk of cardiovascular disease was more significant in the patients under 30 years old, whereas the risk of cerebrovascular accidents was more common among the patients over 30 years old (Huang et al., 2016) With the increase in methamphetamine use, it is most likely that the rates of cardiovascular disease and stroke will increase, especially in the younger methamphetamine users under the age of 30 (Darke et al., 2017; Huang et al., 2016). Additionally, those with pre-existing heart conditions may experience exacerbations caused by methamphetamine use, leading to worse outcomes and more hospitalizations than those with pre-existing heart conditions who do not use illicit substances (Huang et al., 2016). Methamphetamine use has also been linked to complications and worse outcomes in intracerebral hemorrhage cases. Zhu et al. 2020 conducted a study looking at the differences in clinical presentations and outcomes in spontaneous intracerebral hemorrhage (ICH) cases in methamphetamine users (Meth-ICH) vs. Non-Meth-ICH. The study looked at patients with ICH between January 2011 and December 2017. The groups were defined by a history of methamphetamine use and a positive urine drug screen for methamphetamine at the time of admission. Among the 677 patients, 61 were identified as Meth-ICH and 350as Non-Meth-ICH. The Meth-ICH group was more often younger and had a stronger history of tobacco smoking. In contrast, the Non-Meth-ICH group was likelier to have a history of uncontrolled hypertension and antithrombotic use. There was no significant difference in hospital length of stay, clinical severity, rate of functional independence upon discharge, or mortality between the two groups. Since ICH is preventable with proper preventative health care and since the Meth-ICH group was younger, this study shows that methamphetamine use is correlated with worse quality of life compared to non-methamphetamine use (Zhu et al., 2020). While in this study, the Non-Meth-ICH group was found to have higher rates of uncontrolled hypertension, the vascular changes noted in methamphetamine users might play a role in causing cerebrovascular events(Zhu et al., 2020). While most of the complications of methamphetamine use appear to result in immediate cardiovascular, renal, and neurological complications, methamphetamine use might also be linked to long-term complications due to neuronal damage. Recent clinical studies have found that methamphetamine use puts individuals at an increased risk of developing Parkinson’s disease, likely through the formation of the toxic metabolite 6-hydroxy-dopamine and its associated oxidative stress. He et al. in 2022 set out to find the cause of methamphetamine-mediated dopaminergic neuronal damage. This study was conducted using an animal model of mice treated with four days of methamphetamine or a D1 receptor agonist known as SKF38393. This was done on two groups of mice, one with no gene knockouts and the other with a D1 receptor gene knockout. After treatment, cellular indices of autophagy such as LC2, P52, Beclin-1, tyrosine hydroxylase, and the AMPK/FOXO3A pathway were analyzed in the striatal tissue of treated mice. They also analyzed PC12 cells in vitro to determine if there was D1 receptor-mediated activation of autophagy. Researchers found that repeated treatment with high-dose methamphetamine induces dopaminergic neurons and autophagy activation in the striatum of the non-knockout mice, increasing expression of LC3 and P62 signaling activation of autophagy pathways in the striatum. In knockout mice, treatment did not induce either loss of dopaminergic neurons or activation of autophagy pathways. The PC12 cells in vitro confirmed that D1 receptor activation via SKF38393 could lead to activation of autophagy through the AMPK/FOXO3A pathway. It is presumed that the overactivation of D1 receptors plays an important role in dopaminergic neuronal damage and neurotoxicity in patients with long-term methamphetamine use (He et al., 2022) Many other studies have documented that methamphetamine can dysregulate and deplete the brain of dopamine, but few have shown clear neurocognitive or other function sequelae of these neurochemical changes. One such study demonstrates a clear association in mice between dopamine loss and impairment in tasks of associative learning and declarative memory. In a preclinical study, mice were exposed to dosing regimens of 3,4-methylenedioxymethamphetamine (MDMA), methamphetamine, or parachloroamphetamine (PCA) and the authors looked at deficits in learning and memory via passive avoidance behavior and changes in the tissue content of monoamine neurotransmitters and their metabolites in the striatum, frontal cortex, cingulate, hippocampus, and amygdala (Murnane et al., 2012) The authors found that exposure to methamphetamine and PCA had impaired performance in the passive avoidance behavior tests and significant depletions of dopamine, serotonin, and their metabolites in several brain regions.40 Previous research has also linked the Sigma 1 receptor as a major mediator of methamphetamine-induced persistent dopamine dysregulation, as well as having a role in acute toxic effects of methamphetamine – inducing its capacity to induce convulsions, seizures, and mortality. Methamphetamine has also been linked to gastrointestinal mucosa damage. Yang et al. in 2021 set out to find if there were any alterations in the gut microbiome of chronic methamphetamine use. They collected fecal samples from 16 patients treated for SUD at Wuhan Mental Health Center in China. The samples were then analyzed via polymerase chain reaction (PCR), and researchers used a statistical Shannon index to determine the diversity of the gut microbiome. They noted a decreased Shannon index in those with chronic methamphetamine use, indicating a lower bacterial diversity when compared to age-matched controls. They also noted an increased concentration of Fusobacteria correlated to the duration of methamphetamine use and found higher concentrations of Bacteroides and Faecalibacterium, which have been correlated to persons with psychotic syndromes including schizophrenia and depression (Yang et al., 2021) Zhao et al. in 2019 aimed to find the cause of the mucosal inflammatory damage seen with methamphetamine use. Researchers theorized that the mechanism of inflammatory injury was due to Nod-like receptor protein 3 (NLRP3) inflammasome overexpression. To support this, they conducted an animal study in which two groups of mice cells were treated with methamphetamine (5 mg/kg) alone or methamphetamine plus MCC950, an NLRP3 inflammasome inhibitor, and apoptotic and proinflammatory factors were measured to determine the extent of mucosal damage in the mice. In the methamphetamine-only group, more apoptosis occurred, as evidenced by decreased transepithelial electrical resistance and higher levels of proinflammatory cytokines such as IL-6, INF-gamma, TNFalpha, and NF-kB. In the methamphetamine plus inhibitor group, lower levels of IL-6, INF-gamma, and TNF-alpha were seen, but both groups had similar levels of NF-kB.42 Alterations in the gut microbiome and intestinal mucosal damage are linked to higher rates of gastrointestinal infection and worse outcomes. It is likely from these studies that higher rates of methamphetamine use will lead to higher rates of hospital-acquired Clostridioides difficile infections as well as more complications of sepsis and death due to these infections (Yang et al., 202; Zhao et al., 2019). 2.3PREFRONTAL CORTEX (PFC) The prefrontal cortex (PFC) is the largest cortical area in the human brain, making up 29% of the whole cerebral cortex. It is located in the frontal lobe, anterior to the primary motor cortex and the premotor cortex. PFC has key roles in defining personality and behavior. It also has many functions including maintaining attention, planning complex movements, controlling emotions, discriminating between good and bad, speech, memory, temporal perception and working memory (Dehn, 2017; Oren et al., 2016; Ustün et al., 2017). 2.3.1Embryology of the Prefrontal Cortex The nervous system in humans first emerges at the beginning of the third gestational week as a neural plate as part of the ectodermal plate. At the fourth week, three dilatation areas called the primary brain vesicles form. These are, in order from anterior to posterior, prosencephalon (forebrain), mesencephalon (midbrain), and rhombencephalon (hindbrain) (Sadler, 2012). At the fifth week, prosencephalon divides into two brain vesicles called the telencephalon and diencephalon. At the end of the fifth week, two lateral diverticula develop, which are named as telencephalic vesicles. These two vesicles are the primordial forms of the cerebral hemispheres (Sadler, 2012; Moore and Persaud, 2003). The developing cerebral hemispheres are initially like three characteristic layers of the neural tube (ventricular, intermediate, and marginal). Later, a fourth layer develops, and the cells in this layer migrate to the marginal layer to form the cortex. The surface of the cerebral hemispheres is initially straight. However, fissures, sulci and gyri begin to form as the brain grows (Sadler, 2012; Moore and Persaud, 2003). 2.3.2Histology of the Prefrontal Cortex PFC is comprised of six laminae that cannot be clearly distinguished from each other histologically. From inside to outside, these laminae are: I. Lamina zonalis: Contains few Cajal horizontal cells. The axons of Martinotti cells located at deep layers, the last branches of the apical dendrites of pyramidal cells, and the last branches of the afferent nerve fibers extend to this lamina. II. Lamina granularis externa: Harbors granular cells and some small pyramidal cells. III. Lamina pyramidalis externa: Harbors loosely arranged pyramidal cells, which generally show an increase in size from outside to inside. The axons of these cells traverse the white matter and reach other cortical regions. The axons of these cells make up the ipsilateral and contralateral cortico-cortical connections. IV. Lamina granularis interna: It is the layer with the highest number of cells. It harbors stellate pyramidal cells and granular cells. V. Lamina pyramidalis interna: Contains less number of cells in comparison to the other laminae. It harbors well-developed pyramidal cells and Martinotti cells. The axons of the pyramidal cells located in this layer send projection fibers to the basal ganglia. VI. Lamina multiformis: Harbors Martinotti cells, fusiform cells and pyramidal cells (Petanjek et al., 2008; Gartner and Hiatt, 2007). 2.3.3Anatomy of the Prefrontal Cortex Frontal lobe constitutes the whole cerebral cortex area anterior to the central sulcus. It has three parts. The narrow area anterior to the central sulcus is primary motor cortex (Brodmann 4), and the area immediately anterior to the primary motor cortex is premotor cortex (Brodmann 6). PFC comprises the anterior part of the frontal lobe, located anterior to the premotor cortex (Fuster 2008; Yeterian et al., 2012). PFC is made of the superior frontal gyrus, medial frontal gyrus, most part of the inferior frontal gyrus, a great portion of the part of superior frontal gyrus and frontal gyrus that is on the inner surface of the hemisphere, and anterior part of cingulate gyrus (Figure 2.2) (Arinci, 2014). Figure 2.2: Showing Superior, medial and inferior frontal gyri, and orbital gyri (Drawing modified from Putz R) (Putz, 2007). There are three parts of PFC, namely the lateral PFC, medial PFC and orbital PFC (orbitofrontal cortex). i. Lateral prefrontal cortex: It is comprised of Brodmann’s areas 8, 9, 10 and 46. Additionally Brodmann’s areas 44, 45 and 47 are also included in the lateral PFC (Figure 2.3) (Abernathy et al., 2010). In literature, some sources cite Brodmann’s areas 45 and 47 as ventrolateral PFC, and Brodmann’s areas 9 and 46 as dorsolateral PFC. Additionally, the frontal pole, which is the most anterior part of the frontal lobe and is Brodmann’s area 10, is named as rostral or polar PFC (Goel et al., 2017; Ueda et al., 2017 ). ii. Medial prefrontal cortex: The medial PFC includes Brodmann’s areas 8, 9, 10 and 12, and Brodmann’s areas 14, 24, 25 and 32 that are located anterior to cingulate gyrus (Figure 2.4) (Abernathy et al., 2010; Córcoles-Parada et al., 2017). iii. Orbital prefrontal cortex [orbitofrontal cortex]: Brodmann’s areas 11, 12, and 13 constitute the orbital PFC (Figure 2.4). Orbital PFC is anterior to the PFC and surrounds the medial orbital sulcus and lateral orbital sulcus (Abernathy et al., 2010; Goel et al., 2017; Ueda et al., 2017). Figure 2.3: Showing Lateral view of Brodmann’s areas 8, 9, 10, 46, 44, 45 and 47. (Pencil drawing modified from Putz R) (Putz, 2007). Figure 2.4: Showing Medial view of Brodmann’s areas 8, 9, 10, 11, 12, 24, 25 and 32 (Pencil drawing modified from Putz R) (Putz, 2007). 2.3.4Functions of Prefrontal Cortex The principle function of PFC is to plan and execute behaviors (Usami et al., 2011). PFC collects information from cortex and subcortical structures, and arranges and controls this information; decides and executes the behavior (Usami et al., 2011). PFC has vital functions including thinking, reasoning, prospective planning, conforming to the learned social rules, politeness, accurate decision making for sustaining life and executing these decisions. Additionally, morale motivation, self-control ability, common sense, taking lessons from errors and feeling empathy are among the functions of frontal cortex (Funahashi, 2015; Pinti et al., 2015). PFC owns a “working memory” feature, which is the ability to follow different kinds of information simultaneously and to immediately utilize this information by combining them with the following thoughts. Another function of PFC is the elaboration of thoughts, that is, developing depth and abstractness to the information (Murray et al., 2017; Kaminski et al., 2017). The working memory of PFC executes the storage of transient memory information, temporal perception, foresight, prospective planning, deciding whether the stimulus is behavioral or not, preparing planned and logical response to the sensory signal, solving complex mathematical, ethical, moral and conscientious problems, and controlling whether behaviors are within moral rules or not (Funahashi, 2015; Pinti et al., 2015; Kaminski et al., 2017;). PFC has been shown to play a key role in remembering old information and recalling new memory (Ozen and Rezaki, 2007). One of the main functions of PFC, particularly the dorsolateral section is the attention. It blocks distracting external stimuli and allows focusing and giving attention to a particular task (Dehn, 2017). The main function of Brodmann’s area 46 that is included in the dorsolateral PFC is working memory and processing of cognitive information (Ueda et al., 2017; Barth et al., 2016). The main function of Brodmann’s area 11 that is included in the orbital PFC is processing emotions and values. Brodmann’s area 47 that is included in the ventrolateral PFC is functions in feeling empathy, and this area activates during an automatic action. Additionally, Brodmann’s area 47 in the left hemisphere plays a key role in wording function (Barth et al., 2016). Brodmann’s areas 14, 24, 25 and 32 that constitute the medial PFC are parts of the limbic system. Due to the connection of medial PFC with the limbic system, it functions in processing and enhancement of memory. Additionally, medial PFC is associated with most high cognitive functions (Córcoles-Parada et al., 2017). 2.4 HIPPOCAMPUS 2.4.1Anatomy of the Hippocampus Hippocampus is an elongated convex structure deep in the medial temporal lobe and presents an elevation along the floor of the inferior horn of the lateral ventricle (Tatu and Vuillier, 2014; Knierim, 2015; Dekeyzer et al., 2017). The hippocampus has the archipallial cortex and is formed by the infoldings of the dentate gyrus, cornu ammonis and subiculum (Fogwe et al., 2021). The subiculum is continuous with the six-layered neocortex (para-hippocampal gyrus). During development, cornuammonis and dentate gyrus are folded into the inferior horn of the lateral ventricle at the hippocampal sulcus (Laplante et al., 2013); the process brings the outer molecular layers of the dentate gyrus and subiculum close to each other (Fu et al., 2021). The shape of the hippocampus in gross dissection looks like a seahorse (genus Hippocampus) on the basis of which the structure is termed as ‘Hippocampus’. The hippocampus is also known as ‘Ammon’s horn’ because the C-shaped coronal section of the hippocampus resembles ram’s horn; the term ‘Ammon’s horn is derived from the Egyptian deity with ram’s head (Knierim, 2015). The hippocampus is also known as ‘pes hippocampi’ because its anterior bulbous extremity is marked by a number of grooves and the feature resembles a paw of an animal (Parmar et al., 2018). The alveus, a thin sheet of white matter, covers the ventricular surface of the hippocampus (Fogwe et al., 2021; Parmar et al., 2018). The axons of hippocampal pyramidal cells form the alveus; the fibers of the alveus converge at the medial margin of the hippocampus to form fimbria hippocampi (Parmar et al., 2018). The fimbria hippocampi proceed posteriorly covering the dentate gyrus and reach to the splenium of the corpus callosum; thereafter, continues with the fornix (Fogwe et al., 2021). The fimbrio-dentate sulcus separates the fimbria and the dentate gyrus. The fimbria continues as fornix around the thalamus separated by choroidal fissure containing choroidal plexus (Patra et al., 2018). 2.4.2Microscopic Structure The dentate gyrus is the input channel of the hippocampal formation. Histologically, the dentate gyrus consists of three layers, (figure 2.2) from the outside in: the molecular layer, granular layer, and polymorphic layer (Wilczyńska et al., 2018; Ho et al., 2013). The dentate gyrus is semilunar in shape; convexity of which is directed towards the molecular layer while the concavity is directed towards the cornu ammonis (Ho et al., 2013). Figure 2.5: Showing hippocampus, dentate gyrus, subiculum, and entorhinal cortex. A, Coronal section through the hippocampus and dentate Gyrus; B, Schematic diagram to show the histological layers of the dentate gyrus and cornu ammonis.(Chauhan et al., 2021). Dendrites of the granular neurons receive input from the para-hippocampal gyrus (entorhinal cortex) via perforant pathway (figure 2.3) (Ho et al., 2013). The axons of the granular neurons synapse by mossy fibers with the apical dendrites of the pyramidal cells present in the cornu ammonis. The three layers of cornu ammonis (Figure 2.2) can be further subdivided into the following sublayers (Mercer and Thomson, 2017; Mazher and Hassan, 2021): i. Alveus: Efferent fibers from the axons of the pyramidal cells of cornu ammonis form the alveus while some axon collateral re-enters the hippocampus. ii. Stratum oriens: Contains a few inhibitory basket-cell interneurons; two types of basket cells are observed in the stratum oriens. Axons and dendrites of pyramidal cells, recurrent axon collateral, and commissural fibers traverse the stratum oriens. iii. Stratum pyramidalis: Forms the principal cellular component of cornu ammonis. 10- 30 layers of pyramidal cells are present in the stratum pyramidalis; functionally, the pyramidal cells are excitatory. The pyramidal cells have an apex and a base; the base is directed toward the alveus while the apex is directed towards the outer molecular layer. Alveus and fimbria are formed by the axons arising from the center of the base of pyramidal cells. Dendrites arise from the base and apex of the pyramidal cells. The dendrites arising from the base ramify in the stratum oriens and the basal dendrites receive commissural fibers from identical areas of the contralateral hippocampus. Dendrites arising from the apex extend deeper to branch profusely and apical dendrites receive commissural fibers from non-identical areas of the contralateral hippocampus. Apical dendrites also receive afferents from the entorhinal areas and mossy fibers from the dentate gyrus. Recurrent collateral from the neighboring pyramidal cells synapses with the apical dendrites. iv. Stratum radiatum: Comprises apical dendrites of the pyramidal cells and some stellate cells. v. Stratum lacunosum-moleculare: Contains axons and interneurons. Inhibitory interneurons from the stratum lacunosum-moleculare project into the retrosplenial cortex. Figure 2.6: showing the Intrinsic neuronal circuit of the hippocampal formation. A, Papez memory circuit. B, Perforant and alveolar pathway. C, Perforant pathway 2.4.3Organization of Pyramidal Cells in Hippocampus Cornu ammonis of the hippocampus can be subdivided into four regions (Figure 2.2, 2.3): CA1, CA2, CA3, and CA4 (Mazher and Hassan, 2021). The CA1 is the largest region and is delimited laterally by the presubiculum and medially by CA2. Most neurons (90%) of CA1 are pyramidal cells (glutamatergic projection neurons) and the rest (10%) are interneurons. CA2 layer is present towards the dentate gyrus bounded laterally by the CA1 and medially by the CA3 layer. CA2 of the cornu ammonis receives input from the supramammilary region of the hypothalamus but lacks input mossy fires from the dentate gyrus. CA3 layer is directed towards the hilus of the dentate gyrus and is limited medially by the CA2 layer. The superficial most cells of the CA3 are described as CA4 by some authors. The apical dendrites of the CA3 layer receive mossy fibers from the granule cells of the dentate gyrus. Axons of the CA3 pyramidal cells contribute to the alveus, fimbria, and fornix. Some CA3 axons give collateral fibers known as the Schaffer’s collaterals which synapse with the dendrites of CA1 pyramidal cells. Axons from the CA1 pyramidal cells are connected to the subiculum neurons. Axons from the subiculum neurons contribute to form the fibers of the fimbria and fornix via the alveolar pathway (Figure 2.2, 2.3) 2.4.4Connections of Hippocampus 1. The hippocampus receives afferents from the following structures Cingulate gyrus via cingulam. 2. The indusium griseum and septal nuclei through the fornix. 3. Contralateral hippocampus through the hippocampal commissure. 4. Outer part of the entorhinal area through the perforant pathway. 5. Inner part of the entorhinal area and subiculum to the alveus through the alveolar pathway. Efferent fibers from the hippocampus are connected to the following areas through the fornix (Carlson et al., 2021; Li et al., 2021; Vorkapic et al., 2021; Yu et al., 2021; Martens et al., 2021; Ballotta et al., 2021): • Gyrus fasciolaris, indusium griseum, cingulate gyrus, and septal nuclei through the fibers of the dorsal fornix. • Paraterminal gyrus, pre-optic, and anterior hypothalamic nuclei through the precommissural fornix. • Anterior nucleus of thalamus, hypothalamic nuclei, mammillary body through the post-commissural fibers. • Habenular nucleus via stria medullaris thalami 2.4.5FORNIX Fornix forms efferent projection fibers of the hippocampus and also comprises commissural fibers connecting the hippocampus of both sides (Cascella and Al Khalili, 2021). 2.4.5.1Formation of Fornix Axons of pyramidal cells from the cornu ammonis with a major contribution from the subicular complex forms the alveus. Alveus continues as a fimbria which is separated from the dentate gyrus by the fimbrio-dentate sulcus. Continuation of the fimbria is called the fornix (Pradip et al.,-Course of Fornix Fornix is divided into dorsal fornix and ventral fornix at the splenium of the corpus callosum. The dorsal fornix runs along with the gyrus fasciolaris and indusium griseum surrounding the outer surface of the corpus callosum. The ventral fornix runs forward below the splenium of the corpus callosum and around the pulvinar of the thalamus. Ventral fornix forms a pair of crura that run forwards and converge to form the body of the fornix. Commissural fibers connect the medial margins of both crura through the commissure of the fornix (hippocampal commissures). Hippocampal commissures and the crura of the fornix are separated from the body of the corpus callosum by a space known as the ventricle of the fornix (Pradip et al., 2021). 2.4.5.3Body of the Fornix The body of the fornix is a triangular structure having two symmetrical halves and the apex of which is directed in front. Bilaminar septum pellucidum connects the superior surface of the body of the fornix to the body of the corpus callosum. The inferior surface is related to the ependymal roof of the third ventricle and the thalamus separated by tela choroidea (folding of pia mater containing the choroidal plexus) and a pair of internal cerebral veins. Laterally, choroidal fissure separates the body of the fornix from the thalamus. At the anterior interventricular foramen, the body of the fornix diverges into a pair of columns of the fornix. The anterior commissure separates the fibers of the column into pre-commissural fornix and post-commissural fornix. The pre-commissural fornix which mainly contains axon fibers from the CA3 pyramidal cells continues with the para-terminal gyrus. The post commissural fornix forms the anterior boundary of the interventricular foramen and then turns downwards and backwards beneath the ependyma of the third ventricle to reach the mammillary body (Pradip et al., 2021). 2.4.6.HABENULAR NUCLEI The habenular nuclei are pairs of nuclei located in the habenular trigone. The habenular trigone is a depression on either side of the pineal stalk bounded by the stria medullaris (cranio-medially), superior colliculus (caudally), and pulvinar end of the thalamus (laterally) (Dillingham et al., 2021). The habenular nuclei receive afferents via the stria medullaris thalami from the septal area (subcallosal area) and pre-optic nuclei (hypothalamus), via stria terminalis from the amygdaloid body and via fornix from the hippocampus (Taghipour and Derakhshan, 2018; Dillingham et al., 2021). Some stria medullaris thalami fibers pass through the dorsal lamina of the pineal stalk and connect two habenular nuclei. The commissural fibers connecting two habenular nuclei are known as habenular commissure. The habenular nuclei send efferent fibers to the interpeduncular nucleus through the fasciculus retroflexus of Meynert. The interpeduncular nucleus projects to the dorsal tegmental nucleus. Dorsal longitudinal fasciculus connects the dorsal tegmental nucleus to the autonomic and reticular nuclei of the brainstem (Vann and Nelson, 2015). 2.4.7MAMMILO-TEGMENTAL TRACT The mammilo-tegmental tract influences the brainstem and spinal cord for integrated motor response (Vann and Nelson, 2015). The mammilo-tegmental tract is a collection of efferent fibers from the mammillary body to the midbrain tegmental nuclei. Efferent fibers from the tegmental nuclei reach the reticular nuclei as reticulo-bulbar and reticulo-spinal tract (Taghipour and Derakhshan, 2018; Dillingham et al., 2021). 2.4.8MAMMILO-THALAMIC TRACT The mammilo-thalamic tract connects the mammillary body to the anterior nucleus of the thalamus (Vann and Nelson, 2015). The efferent fibers from the anterior nucleus of thalamus reach the cingulate gyrus. From the cingulate gyrus, some fibers project back to the hippocampus. 2.4.9HIPPOCAMPAL FORMATION AND MEMORY One of the functions of the hippocampus is converting short-term memory into long-term memory. The long-term memory is maintained by a unidirectional progression of synaptic connections through the intrinsic hippocampal circuitry (Figure 2.3) (Pradip et al., 2021). 2.4.9.1Papez Circuit Hippocampal circuitry postulated by Papez is called as ‘Papez circuit’ and is adapted as a memory circuit. The Papez circuit is believed to be responsible for emotional integration and for recent memory trace (Tatu and Vuillier, 2014 ;Dillingham et al., 2021). The Papez circuit includes the hippocampus, fimbria, fornix, mammillary body, anterior nucleus of the thalamus and cingulate gyrus (Figure 2.3A). The entorhinal cortex receives sensory information from the association areas of the frontal lobe, parietal lobe, and temporal lobe. The information is converted into memory through the Papez circuit. Short-term memory (episodic memory) is facilitated by unidirectional activation of synaptic connection as follows: 1) Activation of the entorhinal cortex (parahippocampal gyrus) by input from the neocortex and limbic system. 2) Stimuli pass from the entorhinal cortex to the dentate gyrus via perforant path and then pass through the CA3 area. 3) Schaffer’s collaterals from the CA3 transfer stimuli to the CA1; then CA1 efferent synapse at the subiculum. 4) Efferent fibers from the subiculum again project back to the entorhinal cortex. Connections between the CA3 and dentate gyrus, and between CA1 and CA3, lack feedback loop. Alveus, fimbria, and the fornix formed by the axon fibers of the CA3 pyramidal cells and neurons of the subiculum are the major hippocampal output. Fibers from the precommissural fornix (derived from the CA3) are connected to the lateral septal nucleus, and fibers of the post-commissural fornix (derived from the subiculum) are connected to the mammillary bodies and the hypothalamic nuclei. 2.4.9.2Rapid Formation of New Memory Long-term potentiation (LTP) is a mechanism for the rapid formation of new memory. The long-term potentiation increases synaptic efficiency following the high frequency activity of the pre-synaptic terminal. This mechanism involves Schaffer’s collaterals and mossy fibers of the hippocampus. This effect lasts for many days leading to increased activity of the postsynaptic neurons. The high frequency activity is responsible for the accumulation of the calcium ions in post-synaptic neurons which triggers the LTP. Even though the original external stimulus has stopped, the impulses are transmitted frequently from the synapses of the hippocampal formation in the LTP. 2.4.9.3Spatial Memory The hippocampus contains cells (place cells) encoding the spatial memory; these cells are responsible for recalling a place and recalling a route to reach the place. 2.4.9.4Sommer’s Sector Large pyramidal cells of the CA1 (an area known as Sommer’s sector) are extremely sensitive to the oxygen lack; these cells necrose within a few minutes in compromised blood supply. In a condition leading to cerebral ischemia, the subject may lose the memory of the preceding few hours of the incident. 2.4.10BLOOD SUPPLY AND DRAINAGE OF HIPPOCAMPUS Hippocampus plays an important role in the formation of memory, and the dysfunction of the hippocampus leads to neurological disorders like Alzheimer’s disease and epilepsy (Rusinek et al., 2011). Hippocampus is supplied by the branches of the posterior cerebral artery and anterior choroidal artery (Isolan et al., 2020). 2.4.10.1Posterior Cerebral Artery Posterior cerebral artery is the terminal branch of the basilar artery. It joins with the posterior communicating artery to complete the circle of Willis (Isolan et al., 2020). The posterior cerebral artery can be divided into four segments, and it gives three major branches: cortical branches, posterolateral striate branches, and posterior choroid artery (Rusinek et al., 2011; Isolan et al., 2020). The second part of the posterior cerebral artery (from the posterior communicating artery to the posterior margin of the midbrain) gives the anterior inferior temporal artery and the anterior hippocampalparahippocampal artery which supply the entorhinal area (Rusinek et al., 2011; Isolan et al., 2020). The posterior parahippocampal artery arises from the posterior inferior temporal artery (branch of the posterior cerebral artery). The parietooccipital arterial trunk (the branch from the fourth part of the posterior cerebral artery) also supply the parahippocampal gyrus and hippocampus. 2.4.10.2Anterior Choroid Artery Anterior choroid artery is the branch of the internal carotid artery. It originates from the distal part of the internal carotid artery, just after the origin of the posterior communicating artery, and runs into the subarachnoid space. The segment of the anterior choroid artery up to the inferior horn of the lateral ventricle is known as cisternal segment. The anterior choroid artery gives branches to form the choroid plexus for the inferior horn of the lateral ventricle. In addition, the anterior choroid artery also supplies the optic tract, the uncus, the globus pallidus, lateral geniculate body, and the internal capsule. The perforating branches of the anterior choroid artery supplies the amygdala and the hippocampus (Isolan et al., 2020). The anterior hippocampal arteries supply the uncus and the head of the hippocampus, while the posterior hippocampal arteries supply the body and tail of the hippocampus (Isolan et al., 2020). The anterior hippocampal arteries (branch of the anterior inferior temporal artery) enter into the uncal sulcus and supplies the head of the hippocampus. It emerges on the surface of the pyriform lobe and supplies to the adjacent entorhinal area. The posterior hippocampal arteries run in the superficial course of the hippocampal sulcus; along the terminal segment, it gives longitudinal large and small branches. The large branches penetrate the hippocampus and small branches supply the margo denticulatus and fimbriodentate sulcus. The longitudinal branches form a rich anastomosis along the hippocampal sulcus (Rusinek et al., 2011). The intrahippocampal arteries (deep branches) can be classified as large ventral, small ventral, large dorsal, and small dorsal branches. The large ventral intrahippocampal arteries supply stratum lacunosum, stratum pyramidalis, molecular layer of dentate gyrus, and CA1 and CA2 region of the cornu ammonis. The large dorsal intrahippocampal arteries supply the granular layer of the dentate gyrus, and CA3 and CA4 regions of the cornu ammonis. The small ventral intrahippocampal arteries supply the proximal part of the dentate gyrus. The small dorsal intrahippocampal arteries (also known as straight arteries) runs into the fibriodentate sulcus and supplies the adjacent areas. 2.4.10.3Venous Drainage The deep hippocampal veins (intrahippocampal veins) are two types: sulcal intrahippocampal veins and subependymal intrahippocampal veins. The sulcus intrahippocampal veins originate from the CA1 and CA2 areas and reaches the superficial hippocampal sulcus and receive tributaries from the stratum moleculare. The subependymal intrahippocampal veins can be observed on the ventricular surface of the hippocampus. The deep hippocampal veins of CA2 and subiculum drain into the subependymal intrahippocampal veins (Rusinek et al., 2011; Isolan et al., 2020). Superficial hippocampal veins form two longitudinal superficial venous arcade to cover the fimbriodentate sulcus and the superficial hippocampal sulcus. The venous arcade of the fimbriodentate sulcus receives subependymal intrahippocampal veins. The venous arcade of the superficial hippocampal sulcus receives deep intrahippocampal veins (Rusinek et al., 2011; Isolan et al., 2020). Both longitudinal superficial venous arcades unite at the anterior and posterior ends. Anterior end drains into the inferior ventricular vein and posteriorly drain into medial atrial vein. Inferior ventricular vein and medial atrial vein drain into the basal vein. The blood vessels supplying the hippocampus have small calibers and are more prone to thrombus formation. The thrombosis of the hippocampal arteries leads to damage and death of pyramidal neurons of the hippocampus which is characteristic for Alzheimer’s disease. 2.5CEREBELLUM 2.5.1Structure of the Cerebellum At the level of gross anatomy, the cerebellum consists of a tightly folded layer of cortex, with white matter underneath and a fluid-filled ventricle at the base. At the microscopic level, there are four deep nuclei embedded in the white matter. Each part of the cortex consists of the same small set of neuronal elements, laid out in a highly stereotyped geometry. At an intermediate level, the cerebellum and its auxiliary structures can be separated into several hundred or thousand independently functioning modules called “microzones” or “microcompartments”. (wright et al., 2016). Figure 2.7: showing cerebellum and pons. The drawimg of the human brain Gray's Anatomy, Public Domain. (wright et al., 2016). 2.5.2The Gross Anatomy of the Cerebellum The cerebellum is located in the posterior cranial fossa. The fourth ventricle, pons and medulla are in front of the cerebellum (Susan standard, 2008). It is separated from the overlying cerebrum by a layer of leathery duramater, the tentorium cerebelli; all of its connections with other parts of the brain travel through the pons. Anatomists classify the cerebellum as part of the metencephalon, which also includes the pons;the metencephalon is the upper part of the rhombencephalon or “hindbrain”. Like the cerebral cortex, the cerebellum is divided into two hemispheres; it also contains a narrow midline zone (the vermis). A set of large folds is, by convention, used to divide the overall structure into 10 smaller “lobules”. Because of its large number of tiny granule cells, the cerebellum contains more neurons than the total from the rest of the brain, but takes up only 10% of the total brain volume (Llinas et al., 2004). The number of neurons in the cerebellum is related to the number of neurons in the neocortex. There are about 3.6 times as many neurons in the cerebellum as in the neocortex, a ratio that is conserved across many different mammalian species (Herculano-Houzel, 2010). The unusual surface appearance of the cerebellum conceals the fact that most of its volume is made up of a very tightly folded layer of gray matter: the cerebellar cortex. Each ridge or gyrus in this layeris called a folium. It is estimated that, if the human cerebellar cortex were completely unfolded, it would give rise to a layer of neural tissue about 1 meter long and averaging 5 centimeters wide—a total surface area of about 500 square cm, packed within a volume of dimensions 6 cm × 5 cm × 10 cm.[7] Underneath the gray matter of the cortex lies white matter, made up largely of myelinated nerve fibers running to and from the cortex. Embedded within the white matter—which is sometimes called the arbor vitae (tree of life) because of its branched, tree-like appearance in cross-section—are four deep cerebellar nuclei, composed of gray matter (wright et al., 2016). Connecting the cerebellum to different parts of the nervous system are three paired cerebellar peduncles. These are the superior cerebellar peduncle, the middle cerebellar peduncle and the inferior cerebellar peduncle, named by their position relative to the vermis. The superior cerebellar peduncle is mainly an output to the cerebral cortex, carrying efferent fibers to upper motor neurons in the cerebral cortex. The fibers arise from the deep cerebellar nuclei. The middle cerebellar peduncle is connected to the pons and receives all of its input from the pons mainly from the pontine nuclei. The input to the pons is from the cerebral cortex and is relayed from the pontine nuclei via transverse pontine fibers to the cerebellum. The middle peduncle is the largest of the three and its afferent fibers are grouped into three separate fascicles taking their inputs to different parts of the cerebellum. The inferior cerebellar peduncle receives input from afferent fibers from the vestibular nuclei, spinal cord and the tegmentum. Output from the inferior peduncle is via efferent fibers to the vestibular nuclei and the reticular formation. The whole of the cerebellum receives modulatory input from the inferior olivary nucleus via the inferior cerebellar peduncle (Purves and Dale, 2011). 2.5.3Subdivisions Based on the surface appearance, three lobes can be distinguished within the cerebellum: the anterior lobe (above the primary fissure), the posterior lobe (below the primary fissure), and the flocculonodular lobe (below the posterior fissure). These lobes divide the cerebellum from rostral to caudal (in humans, top to bottom). In terms of function, however, there is a more important distinction along the medial-to-lateral dimension. Leaving out the flocculonodular lobe, which has distinct connections and functions, the cerebellum can be parsed functionally into a medial sector called the spinocerebellum and a larger lateral sector called the cerebrocerebellum (wright et al., 2016). A narrow strip of protruding tissue along the midline is called the cerebellar vermis. (Vermis is Latin for “worm”.)(wright et al., 2016) The smallest region, the flocculonodular lobe, is often called the vestibulocerebellum. It is the oldest part in evolutionary terms (archicerebellum) and participates mainly in balance and spatial orientation; its primary connections are with the vestibular nuclei, although it also receives visual and other sensory input. Damage to this region causes disturbances of balance and gait (wright et al., 2016). The medial zone of the anterior and posteriorlobes constitutes the spinocerebellum, also known as paleo cerebellum. This sector of the cerebellum functions mainly to fine-tune body and limb movements. It receives proprioceptive input from the dorsal columns of the spinal cord (including the spinocerebellar tract) and from the cranial trigeminal nerve, as well as from visual and auditory systems. It sends fibers to deep cerebellar nuclei that, in turn, project to both the cerebral cortex and the brain stem, thus providing modulation of descending motor systems (wright et al., 2016) The lateral zone, which in humans is by far the largest part, constitutes the cerebrocerebellum, also known as neocerebellum. It receives input exclusively from the cerebral cortex (especially the parietal lobe) via the pontine nuclei (forming cortico-ponto-cerebellar pathways), and sends output mainly to the ventrolateral thalamus (in turn connected to motor areas of the premotor cortex and primary motor area of the cerebral cortex) and to the red nucleus (wright et al., 2016). There is disagreement about the best way to describe the functions of the lateral cerebellum: It is thought to be involved in planning movement that is about to occur (Buckner, 2013), in evaluating sensory information for action (wright et al., 2016), and in a number of purely (wright et al., 2016), cognitive functions, such as determining the verb which best fits with a certain noun (as in “sit” for “chair”) (Buckner, 2013). Figure 2.8: Showing the Schematic representation of the major anatomical subdivisions of the cerebellum. Superior view of an "unrolled" cerebellum, placing the vermis in one plane (wright et al., 2016) 2.5.4HISTORY 2.5.4.1Descriptions Even the earliest anatomists were able to recognize the cerebellum by its distinctive appearance. Aristotle and Herophilus (quoted in Galen) called it the parencephalis, as opposed to the encephalon or brain proper. Galen’s extensive description is the earliest that survives. He speculated thatthe cerebellum was the source of motor nerves (wrigt et al., 2016). Further significant developments did not come untilthe Renaissance. Vesalius discussed the cerebellum briefly, and the anatomy was described more thoroughly by Thomas Willis in 1664. More anatomical work was done during the 18th century, but it was not until early in the 19th century that the first insights into the function of the cerebellum were obtained. Luigi Rolando in 1809 established the key finding that damage to the cerebellum results in motor disturbances. Jean Pierre Flourens in the first half of the 19th century carried out detailed experimental work, which revealed that animals with cerebellar damage can still move, but with a loss of coordination (strange movements, awkward gait, and muscular weakness), and that recovery after the lesion can be nearly complete unless the lesion is very extensive (Ito, 2002). By the beginning of the 20th century, it was widely accepted that the primary function of the cerebellum relates to motor control;the first half of the 20th century produced several detailed descriptions of the clinical symptoms associated with cerebellar disease in humans (Fine et al., 2002). 2.5.5Etymology The name cerebellum is a diminutive of cerebrum (brain); it can be translated literally as little brain. The Latin name is a direct translation of the Ancient Greek παρεγκεφαλίς (parencephalis), which was used in the works of Aristotle,the first known writerto describe the structure. No other name is used in the Englishlanguage literature, but historically a variety of Greek or Latin-derived names have been used, including cerebrum parvum, encephalion, encranion, cerebrum posterius, and parencephalis ( wright et al.,-Functions of the Cerebellum The strongest clues to the function of the cerebellum have come from examining the consequences of damage to it. Animals and humans with cerebellar dysfunction show, above all, problems with motor control, on the same side of the body as the damaged part of the cerebellum. They continue to be able to generate motor activity, but it loses precision, producing erratic, uncoordinated, or incorrectly timed movements. A standard test of cerebellar function is to reach with the tip of the finger for a target at arm’s length: A healthy person will move the fingertip in a rapid straight trajectory, whereas a person with cerebellar damage will reach slowly and erratically, with many mid-course corrections. Deficits in non-motor functions are more difficult to detect. Thus, the general conclusion reached decades ago is that the basic function of the cerebellum is to calibrate the detailed form of a movement, not to initiate movements or to decide which movements to execute (wright et al., 2016). Prior to the 1990s the function of the cerebellum was almost universally believed to be purely motor-related, but newer findings have brought that view into question. Functional imaging studies have shown cerebellar activation in relation to language, attention, and mental imagery; correlation studies have shown interactions between the cerebellum and non-motor areas of the cerebral cortex; and a variety of non-motor symptoms have been recognized in people with damage that appears to be confined to the cerebellum (Rapp, 2001; Doya, 2000). In particular, the cerebellar cognitive affective syndrome has been described in adults and children (Levisohn et al, 2000). Estimates based on functional mapping of the cerebellum using functional MRI suggest that more than half of the cerebellar cortex is interconnected with association zones of the cerebral cortex (Buckner et al., 2011). Kenji Doya has argued that the function of the cerebellum is best understood not in terms of what behaviors it is involved in, but rather in terms of what neural computations it performs; the cerebellum consists of a large number of more or less independent modules, all with the same geometrically regular internal structure, and therefore all, it is presumed, performing the same computation. If the input and output connections of a module are with motor areas (as many are),then the module will be involved in motor behavior; but, if the connections are with areas involved in non-motor cognition, the module will show other types of behavioral correlates. Thus the cerebellum has been implicated in the regulation of many differing functional traits such as affection, emotion and behavior (Hernáez-Goñi et al., 2010; Turner et al., 2007). The cerebellum, Doya proposes, is best understood as predictive action selection based on “internal models” of the environment or a device for supervised learning, in contrast to the basal ganglia, which perform reinforcement learning, and the cerebral cortex, which performs unsupervised learning (wright et al. 2016). 2.5.7Principles The comparative simplicity and regularity of the cerebellar anatomy led to an early hope that it might imply a similar simplicity of computational function, as expressed in one of the first books on cerebellar electrophysiology, The Cerebellum as a Neuronal Machine by John C. Eccles, Masao Ito, and János Szentágothai. [31] Although a full understanding of cerebellar function has remained elusive, at least four principles have been identified as important: (1) feedforward processing, (2) divergence and convergence, (3) modularity, and (4) plasticity. 1. Feedforward Processing: The cerebellum differs from most other parts of the brain (especially the cerebral cortex) in that the signal processing is almost entirely feedforward—that is, signals move unidirectionally through the system from input to output, with very little recurrent internal transmission. The small amount of recurrence that does exist consists of mutual inhibition; there are no mutually excitatory circuits. This feedforward mode of operation means that the cerebellum, in contrast to the cerebral cortex, cannot generate self-sustaining patterns of neural activity. Signals enter the circuit, are processed by each stage in sequential order, and then leave. As Eccles, Ito, and Szentágothai wrote, “This elimination in the design of all possibility of reverberatory chains of neuronal excitation is undoubtedly a great advantage in the performance ofthe cerebellum as a computer, because what the rest of the nervous system requires from the cerebellum is presumably not some output expressing the operation of complex reverberatory circuits in the cerebellum but rather a quick and clear response to the input of any particular set of information.” (The Cerebellum as a Neuronal Machine). 2. Divergence and Convergence: In the human cerebellum, information from 200 million mossy fiber inputs is expanded to 40 billion granule cells, whose parallel fiber outputs then converge onto 15 million Purkinje cells (Llinas et al., 2004) Because of the way that they are lined up longitudinally, the 1000 or so Purkinje cells belonging to a microzone may receive input from as many as 100 million parallel fibers, and focus their own output down to a group of less than 50 deep nuclear cells (Apps and Garwicz, 2005) Thus, the cerebellar network receives a modest number of inputs, processes them very extensively through its rigorously structured internal network, and sends out the results via a very limited number of output cells. 3. Modularity: The cerebellar system is functionally divided into more or less independent modules, which probably number in the hundreds to thousands. All modules have a similar internal structure, but different inputs and outputs. A module (a multizonal microcompartment in the terminology of Apps and Garwicz) consists of a small cluster of neurons in the inferior olivary nucleus, a set of long narrow strips of Purkinje cells in the cerebellar cortex (microzones), and a small cluster of neurons in one of the deep cerebellar nuclei. Different modules share input from mossy fibers and parallel fibers, but in other respects they appear to function independently—the output of one module does not appear to significantly influence the activity of other modules (Apps and Garwicz, 2005). 4. Plasticity: The synapses between parallel fibers and Purkinje cells, and the synapses between mossy fibers and deep nuclear cells, are both susceptible to modification of their strength. In a single cerebellar module, input from as many as a billion parallel fibers converges onto a group of less than 50 deep nuclear cells, and the influence of each parallel fiber on those nuclear cells is adjustable. This arrangement gives tremendous flexibility for fine tuning the relationship between the cerebellar inputs and outputs (Boyden et al., 2004).