Aphasia is an acquired language disorder following brain injury; dyslexia is a developmental reading disorder. Both affect left-hemisphere language networks but differ in etiology and deficit profiles. This PRISMA-guided review synthesizes neuroimaging and behavioral evidence comparing phonological, syntactic, and semantic impairments and their neuroanatomical substrates. PubMed, Scopus, and Web of Science were searched (2015–2024) using aphasia/dyslexia terms combined with phonological/syntactic/semantic and fMRI/DTI/lesion keywords. Two reviewers screened 1,247 records, yielding 47 studies (25 aphasia-based, n=1,248 patients; 22 dyslexia-based, n=1,112 patients). Quality was assessed with Newcastle–Ottawa Scale (mean 7.6/9). Aphasia-based studies showed lesions in left inferior frontal gyrus and insula correlating with phonemic paraphasias (r=0.68, 95% CI 0.42–0.85, p=0.002) and reduced mean length of utterance (3.4±1.2 vs 7.3±0.8 words, p<0.001). Dyslexia-based studies reported reduced superior temporal gyrus–angular gyrus connectivity (t=4.21, p<0.001) with preserved oral syntax but slower reading (4.7±1.1s vs 2.4±0.6s, p<0.001). Limitations include diagnostic heterogeneity, cross-sectional designs, and English-language restriction. Evidence suggests aphasia disrupts dorsal-stream production networks via focal lesions, whereas dyslexia reflects disrupted temporoparietal connectivity underlying phoneme–grapheme integration. Preliminary neuromodulation trials warrant larger randomized controlled studies.
This review synthesizes current evidence on posttraumatic stress disorder (PTSD) neurobiology and evaluates ketamine’s potential mechanisms, with particular emphasis on region-specific alterations in synaptic plasticity and glutamatergic transmission as avenues for symptom relief. PTSD is a complex psychiatric condition characterized by intrusive flashbacks, emotional dysregulation, and heightened physiological arousal following exposure to trauma. Conventional treatments, such as cognitive behavioral therapy (CBT) and selective serotonin reuptake inhibitors (SSRIs), offer some symptom relief but for many patients, efficacy is limited, highlighting the urgent need for alternative interventions. Clinical studies suggest that ketamine can produce rapid reductions in PTSD symptoms, positioning it as a potential intervention for patients who do not respond adequately to conventional treatments; however, the neurobiological mechanisms underlying these effects remain insufficiently understood. Acknowledging this, this review specifically addresses the potential mechanisms by which ketamine may reduce symptoms through the incorporation and exploration of PTSD’s neurobiological underpinnings, aiming to both synthesize the current literature and identify priorities for further research. Overall, current evidence suggests that ketamine may reduce PTSD symptoms through changes in α-amino-3-hydroxy-5-methyl-4-iso xazolepropionic acid receptor (AMPAR) dependent plasticity and modulation of glutamatergic transmission, though questions remain regarding long-term safety, durability, and precise molecular mechanisms.
Alterations in neuronal and glial metabolism contribute to numerous neurodegenerative diseases. The crosstalk between these two cell types (i.e., axoglial metabolic coupling) is at the center of extensive investigation. Metabolic alterations frequently culminate in mitochondrial dysfunction, but it has proven challenging to obtain cell-type-specific metabolic data from neuronal or glial mitochondria in mouse models of injury or disease. Magnetic immunocapture of genetically tagged mitochondria has emerged as a powerful strategy. However, existing immunocapture methods are either incompatible with sensitive downstream multi-omic workflows or not yet tested in complex tissues with highly heterogeneous cell populations. In the present study, the research team refined the MITO-Tag approach, which leverages a Cre-dependent 3×HA-EGFP-OMP25 epitope tag localized to the outer mitochondrial membrane. Following enzymatic and mechanical dissociation, mitochondria were rapidly immunopurified from cortical neurons, oligodendrocyte lineage cells, and—accomplished here for the first time—peripheral nerve axons using anti-hemagglutinin magnetic beads in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-compatible KPBS buffer. This method yields specific and structurally intact mitochondria, as confirmed by live-organelle imaging and Western blotting for compartment-specific markers (COXIV, VDAC, citrate synthase), with minimal contamination from other organelles. Proteomic analysis of brainderived immunoprecipitates revealed mitochondrial enrichment comparable to existing magnetic immunopurification workflows. As the first isolation strategy which enables multi-omic profiling of mitochondria from lower-abundance cell types, this method provides a versatile tool for investigating mitochondrial involvement in neurological disease and injury in vivo. Notably, isolation of axonal mitochondria permits characterization of the metabolic shifts occurring in axons after injury and in disease models, including shifts associated with axoglial metabolic coupling. Given that axon death is preceded by bioenergetic failure, defining the mitochondrial mechanisms involved could reveal novel targets for neuroprotective therapies.
This manuscript provides a comprehensive overview of the role of auto-associative networks (AANs) in the neurobiological process of cognitive behavior. The structural organization of the brain is largely governed by the Hebbian postulate, “cells that fire together, wire together.” This organization is supported by assemblies that use both ascending feedforward and descending feedback projections, as well as recursive loops that allow for the enhancement of human capacity for such cognition. Detailed analysis reveals that AANs enable the prime and functional retrieval of human memory and language. Their attractor system facilitates pattern completion from noisy and partial cues, enabling reliable recall of stored information. This winner-take-all approach calls for the closest match to be pulled from prior expectations. Oscillatory controls, long-term potentiation, and NMDA gating all contribute to this process, pushing our brains to not only replay events but also predict and simulate those of the future. This can be conceptualized from a symbolic standpoint using real-world items (RWIs), tokens that represent abstractions and can be paired via spreading activation. AANs support language by organizing symbolic RWIs into stable, retrievable assemblies. This context suggests that these associative dynamics are the foundation for driving the fluidity of human memory, language, and predictive thought.
Prescription stimulants such as medication for Attention-Deficit Hyperactivity Disorder (ADHD) can be detrimental when misused. Illicit ADHD medication usage, or non-prescription use, is prevalent across college students in pursuit of an academic advantage, increased focus, and recreation. Similarly, cannabis use has increased among college students, often as a result of legalization in various states. The purpose of this study was to investigate associations among prescribed and illicit ADHD medication use, grade point average (GPA) and mental distress as well as the relationship among cannabis use, mental distress and grade point average (GPA). Responses from 702 undergraduate college students from universities across the United States were included. An anonymous cross-sectional survey assessed self-reported drug usage, GPA and mental health. The results of the study revealed that 71.4% of study participants who reported ADHD medication use were using the medication illicitly. Statistically significant (p<0.01) positive correlations between illicit ADHD dependence and both a marked decrease (~ -1.0) and slight decrease (~ -0.5) in GPA were found,. There was a positive correlation among ADHD medication use and feeling worthless (p<0.05) as well as a negative correlation among very frequent use (40+ times annually) and GPA (p<0.01). While non-frequent cannabis usage (no usage in the last 12 months) showed negative correlations with symptoms of mental distress (p<0.05), very frequent cannabis usage showed positive correlations with mental distress (p<0.05). The findings suggest that ADHD medication usage has a negative impact on academic performance and that very frequent use of cannabis has a negative impact on mental well-being. Study results add to understanding of the relationship among prescribed and illicit ADHD medication use, GPA, and mental distress as well as cannabis use, GPA and mental distress in college students.
The effects of nap timing and sleep rebound on short-term memory following traumatic brain injury (TBI) in Drosophila melanogaster were investigated. Optimal sleep timing post-TBI was explored as a potential therapeutic avenue for enhancing recovery and mitigating long-term cognitive impairments. Although current medical advice often recommends staying awake after a TBI to prevent secondary injury, emerging research now suggests that napping post-injury may engage protective mechanisms that help to prevent cognitive impairment. It is hypothesized that sleeping soon after a TBI optimizes cognitive recovery, leading to improved learning and memory in contrast to late sleep onset. Using a high-impact trauma (HIT) device to induce a TBI, a blue light protocol to induce sleep deprivation, and a taste aversion protocol to assess memory, TBI flies are shown to have significant learning and memory deficits when their sleep is disturbed immediately before bedtime. However, when their sleep is disturbed earlier in the day, TBI flies only show memory deficits, but not learning impairments. This study highlights the potential role of sleep rebound in cognitive recovery, with early sleep disturbance leading to improved learning outcomes potentially due to an earlier sleep rebound effect. The specific mechanisms underlying why earlier blue light disturbance improved cognitive function after TBI remains unclear.
Individuals with Autism Spectrum Disorder (ASD) exhibit wide variations in symptoms, cognitive abilities, and levels of impairment. Recent studies reveal individual-specific connectivity patterns and their association with specific clinical features. Advances in structural and functional connectivity analyses have allowed for a better understanding of the heterogeneity in ASD influenced by factors like age, symptoms, and genetics, indicating its potential for unveiling the diverse brain-behavior relationships in ASD.
Alcohol misuse is a leading cause of preventable death, yet the neurochemical changes governing its addictive properties remain elusive. Ethanol, the psychoactive component of alcoholic beverages, interacts with many neurotransmitter systems, including dopamine. Dopamine is a key modulator of reward and its dysregulation represents one mechanism behind ethanol’s reinforcing effects. After release, dopamine is cleared from the synapse by uptake, diffusion, and enzymatic degradation. These processes maintain dopamine homeostasis and thus modulate neural activity. Proteins which aid in the removal of dopamine from the synaptic cleft are associated with maladaptive ethanol-seeking behaviours. However, studies investigating whether ethanol administration directly affects dopamine clearance mechanisms are unclear. Deciphering how ethanol modulates synaptic dopamine clearance will provide insight into the mechanism of action of ethanol, and reveal new drug targets for alcohol use disorder. This review deciphers whether ethanol modulates dopamine clearance, and considers potential mechanisms regarding ethanol-induced changes in dopamine clearance, with a focus on dopamine uptake and degradation. Finally, this review highlights existing gaps and controversies in the literature, and emphasizes the use of neurogenetic approaches in Drosophila melanogaster to resolve outstanding questions.
Alzheimer's disease (AD) presents a significant challenge to global healthcare systems due to its progressive nature and lack of effective treatments. This meta-analysis explores the role of exercise and sleep in influencing the risk and progression of AD. Through an analysis of numerous studies, it becomes apparent that lifestyle factors such as physical activity and sleep duration play crucial roles in the development and management of AD. Exercise interventions can positively impact motor, cardiovascular, memory, cognitive, and brain volume outcomes in older adults, potentially mitigating the risk of cognitive decline in AD. Furthermore, sleep changes, including alterations in sleep duration and light exposure, can affect biomarkers associated with AD pathology. By understanding the connection between exercise, sleep, and AD, healthcare professionals can develop more comprehensive strategies for preventing and managing this disease.
Expanding access to scientific education for the public is crucial for both young children and adults. Those deprived of educational opportunities often lack a strong connection to scientific disciplines, particularly neuroscience. A profound understanding of brain functions can be inspiring, especially when taught by young neuroscientists. Outreach efforts not only expose college students to aspects of the workforce beyond traditional classrooms but also promote a broader understanding of the social determinants of health across all demographics. Food insecurity remains a major concern in the U.S., impacting children's health and development. Local initiatives such as food drives and pantries play a crucial role in alleviating food insecurity, as poor nutrition can contribute to emotional and behavioral challenges. Malnourishment exacerbates various health issues, emphasizing the importance of public education on nutrition and access to nutritious foods. Ensuring access to education and informational sources about nutrition is particularly vital for individuals facing disparities. Establishing information centers offering diverse educational resources pertaining to health and safety can bridge gaps caused by a lack of information, as well as incorporating community service into STEM curricula. Providing this information can be instrumental in addressing challenges, such as bridging the gap between academic learning and real-world applications. By educating the public, neuroscientists can foster a greater understanding of the field and its impact on society, ultimately making neuroscience more accessible and relevant to broader audiences. This article explores effective community-centered approaches that benefit both the public and students, while highlighting the role of pedagogical strategies in achieving these goals.
Nicotine is a highly addictive drug that is associated with numerous negative health outcomes and remains a significant public issue. Current nicotine addiction treatments remain insufficient, with high noncompliance and relapse rates. This study explores the potential of three FDA-approved drugs that may have clinical relevance to treat nicotine addiction. We used Caenorhabditis elegans (C. elegans) as a model organism to investigate the effects of levodopa (L-DOPA), naloxone (NXN), and N-acetylcysteine (NAC) on nicotine preference. Nicotine preference was assessed using a behavioral chemotaxis choice assay, where C. elegans were placed in the middle of an agar well and allowed to move either to an area with nicotine (Zone A) or vehicle (Zone B). In this study, 30 minute pretreatment with the dopamine precursor L-DOPA decreased nicotine preference. Similarly, a 30 minute pretreatment with the opioid receptor antagonist NXN decreased nicotine preference. However, neither 30 minute pretreatment nor chronic pretreatment with the antioxidant NAC affected nicotine preference. The current results provide preliminary evidence that both L-DOPA and NXN may be promising pharmacotherapies for reducing nicotine preference and helping individuals fight nicotine addiction. Future studies are therefore needed to evaluate this clinical potential, especially in a chronic nicotine exposure setting.
The integration of artificial intelligence with neuroimaging presents a transformative approach to medical diagnostics, enhancing the accuracy and efficiency of disease detection and prognosis. This paper presents a comprehensive overview of the application of AI techniques to neuroimaging data, encompassing the full workflow from data acquisition and preprocessing to model development and evaluation. Various neuroimaging modalities, including MRI, fMRI, and PET, are examined alongside the essential preprocessing steps required for AI-based analysis. Key tools and techniques for data manipulation and visualization are also highlighted. The role of deep learning models, particularly convolutional neural networks, in interpreting neuroimaging data is explored in depth. Practical applications in conditions such as Alzheimer’s disease, epilepsy, and Parkinson’s disease demonstrate the real-world potential of these approaches in clinical diagnosis, prognosis, and treatment. Ethical considerations, including data privacy and algorithmic bias, are also discussed. The goal of this paper is to inform and support further advancements in the interdisciplinary field of AI-enhanced neuroimaging.
Concussion is a leading form of mild traumatic brain injury (mTBI) in the general population, causing a range of symptoms and recovery times in those it affects. Studying the impact of concussion on the brain is an important area of neurological and psychological research, with varying methods of study including electroencephalogram (EEG). This study seeks to understand how mTBI affects the attention of college-aged students, studying their EEG brain waves during attentional tasks from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). We found that individuals who had previously experienced concussion(s) had an increase in beta wave activity, suggesting an increased need for focus during attentional tasks. Beta waves are associated with alertness, active thinking, and attention, indicating a disparity in function for those who have experienced concussion. Additionally, the attention index scores of concussed individuals significantly decreased, suggesting a decline in executive functioning capabilities over a long stretch of time with sustained attention.
This study investigates the preferences, experiences, and career aspirations of undergraduate neuroscience students at Augusta University (AU), focusing on the Diversity Integrated Program in Neuroscience (DIP-IN). Aimed at addressing the gap between student expectations and the offerings of AU's neuroscience program, the research highlights disparities in access to educational opportunities for underrepresented groups, affecting both research and healthcare equity. Survey findings reveal that students prioritize flexible academic options available year-round and seek a curriculum integrating traditional neuroscience topics with practical skills and technological advancements. These results underline the need to refine DIP-IN to create a more inclusive, equitable, and effective learning environment. By aligning the program with student needs, AU's neuroscience program can address these gaps and better prepare MSTEM students for the evolving demands of the neuroscience field.
Depression and anxiety are mood disorders that affect approximately 280 to 301 million people worldwide. These disorders are characterized by excessive worry, low self-esteem, depressed mood, and other symptoms that impact daily life. In addition, depression and anxiety are often comorbid, with depression exacerbating anxiety symptoms and anxiety worsening depression. This study examined the potential antidepressant and anxiolytic properties of olive oil and oleuropein, a phenolic compound found in olive oil. Both olive oil and oleuropein may be feasible naturopathic treatments for depression and anxiety due to their ability to reduce oxidative stress in the brain. Additionally, they may promote the levels of noradrenaline, adrenaline, and BrainDerived Neurotrophic Factor. This study used three groups of six Sprague-Dawley rats, (n=18) selectively bred for genetic predispositions to depression and anxiety-related behaviors, (i.e., the Swim Low-Active line). One group was fed oleuropein daily, another was fed olive oil, and the last group remained on a control diet for 57 days. Data was analyzed using a one-way analysis of variance, utilizing data from three behavioral tests: the Open-Field Test, the Porsolt Swim Test, and the Elevated Plus Maze. The olive oil group exhibited significantly less anxiety-related behavior than the control group in the Elevated Plus Maze, which was measured by recording the number of times the rats entered the open arms of the maze. These findings indicate that olive oil had more effective anxiolytic effects than oleuropein in rodent models. However, there were no significant changes in depression-related behavior in either the experimental or control groups observed during the Porsolt Swim Test. These results support the potential naturopathic benefits of olive oil in treating anxiety. This paper also opens the door for further research into the interactions of specific phenolic compounds found in olive oil to reduce oxidative stress.
Proteins play a crucial role in the human body, and there has been a continuous effort to investigate the three-dimensional structures of proteins for a better understanding of the human body. For example, if a certain type of protein has a similar structure, it can be inferred that these proteins all have similar functions to others. The VAP proteins are integral adaptor proteins of the endoplasmic reticulum (ER) membrane that recruit a myriad of interacting partners to the ER surface. In 2004, it was discovered that a mutation (p.P56S) in the VAPB paralogue causes a rare form of dominantly inherited familial amyotrophic lateral sclerosis (ALS8). That's why it's important to study the structure of the VAP protein, which will help us understand the causes of ALS8 and develop new treatments. AlphaFold, a deep learning algorithm, has demonstrated outstanding accuracy in predicting the three-dimensional structures of unknown folded proteins compared to other tools. Consequently, it has been widely applied in various fields such as biology and medicine to contribute to the advancement of human society. AlphaFold could be used to predict virus structure and accelerate artificial intelligence powered drug discovery. Furthermore, building upon AlphaFold, the newly developed AlphaFold2-multimer offers insights into the processes of protein-protein interactions. The aim of this review is to compare the structure of the VAPB protein determined by conventional X-ray methods with that predicted using AlphaFold, and by utilizing AlphaFold to study the interactions between VAP proteins and a number of proteins containing FFAT sequences, which facilitates drug discovery and development for ALS disease.
Alzheimer’s Disease (AD) is characterized by progressive cognitive decline, and the prevalence of the disease continues to rise as the population ages. With over 10 million new cases yearly, there is still no established way to quickly and accurately classify the stage a patient with Alzheimer’s has progressed to. Inaccurate classification leads to delayed or incorrect treatment, financial implications, and emotional distress, ultimately severely impacting both the patient and their family. This paper proposes a novel deep learning Convolutional Neural Network (CNN) model to assist in the classification of a patient's AD. The model development began with a dataset of over 6,000 pre-labeled Magnetic Resonance Imaging (MRI) scans categorized into four stages: normal, very-mild, mild, and moderate AD. Using transfer learning with InceptionV3, the framework analyzes class proportions and fine-tunes a CNN to leverage pre-trained features. The CNN then undergoes rigorous training with early stopping based on validation Area Under Curve (AUC). Upon completing the training, a comprehensive model evaluation is conducted, encompassing metrics such as AUC and confusion matrices. Using these methods, three different mini-models are constructed, all with different training parameters and number of epochs. Conclusively, the successful models are saved, and an array of evaluation metrics such as accuracy, precision, F1 score, and recall are then used to analyze the models' results. The results show that the proposed method achieves 95.0% or more across all metrics in seconds, demonstrating a promising classification performance that can greatly assist doctors in the classification of AD, helping achieve not only higher diagnostic accuracy but also allowing for faster diagnosis. The results of this model demonstrate significant potential to enhance patient care and highlight the impact of Artificial Intelligence (AI) on AD diagnosis. With the ability to classify MRIs with 20 percent greater accuracy and reduce diagnostic time from months to seconds, the promise of AI in not only AD diagnosis, but the whole of healthcare will enable us to take steps toward a better world. Envisioning a future where the healthcare system can prioritize patient wants rather than just needs. Abbreviations: AD - Alzheimer’s Disease; AI - Artificial Intelligence; AUC – Area Under Curve; CNN - Convolutional Neural Network; RNN - Recurrent Neural Networks; ROC - Receiver Operating Characteristic
Multiple Sclerosis is a complex neurological disorder that presents significant challenges for both patients and healthcare providers. It affects the central nervous system, which includes the brain and spinal cord, leading to a wide range of symptoms and varying degrees of disability and impairment. The disease can manifest in different forms, with some patients experiencing relapsing-remitting episodes while others may face a progressive decline in function. Understanding the underlying mechanisms of multiple sclerosis is crucial for developing effective treatments and improving patient outcomes. One promising area of research focuses on the neurofilament light chain, an essential component of neurons. Recent studies have highlighted its potential role in the context of multiple sclerosis, particularly concerning disease progression, prognosis, and treatment monitoring. Neurofilament light chain levels in the blood and cerebrospinal fluid have been shown to correlate with neuronal damage and disease activity, making it a valuable biomarker for multiple sclerosis. By exploring the structure and function of this protein, researchers hope to gain deeper insights into the pathophysiology of multiple sclerosis and identify new therapeutic targets. This comprehensive review aims to provide an in-depth analysis of the neurofilament light chain and its relevance to the disease. It will cover the current understanding of the disease, including its pathogenesis, clinical manifestations, and the challenges faced in diagnosis and treatment. The review will also discuss the existing diagnostic tools and therapeutic strategies. By examining the latest research on the neurofilament light chain, this review will explore its potential as a diagnostic and prognostic marker, as well as its therapeutic applicability. The ultimate goal of this review is to provide a comprehensive overview of the neurofilament light chain's role in multiple sclerosis and to comprehend its potential to be used as a biomarker for the disease.
The use of anabolic androgenic steroids (AAS), such as testosterone, is associated with a myriad of physiological and behavioral health concerns. Most notably, in both humans and rats, exposure to supraphysiological levels of testosterone has been shown to increase aggressive-like behaviors. However, it is unclear if testosterone enhances the motivation to engage in aggressive-type behaviors to begin with. To elucidate the effects of testosterone on the motivation to seek out a potentially aggressive encounter, rats were tested on a social motivation task in which physical contact between test and stimulus rats was prevented by a wire barrier, which eliminated the possibility of physical provocation. Relative to vehicle treated rats, rats treated with testosterone during adolescence exhibited lower levels of social motivation, as indicted by a reduction in the amount of time spent adjacent to the wire barrier. Over the course of the social motivation test, there were no differences between testosterone and vehicle treated rats in the frequency of rearing behavior and the total number of fecal boli, which suggests that the testosterone-induced reduction in social motivation occurred independent of changes in activity and emotionality, respectively. Consistent with previous studies, testosterone treatment during adolescence decreased total weight gain, increased the weights of the bulbourethral glands, and decreased the weights of the testes. These results suggest that the heightened levels of aggression following testosterone exposure that have been reported in other studies are likely not due to a greater motivation to seek out an interaction with a conspecific of the same sex. Abbreviations: ASS – Anabolic Androgenic Steroids; PND – Post Natal Day; HPA axis – Hypothalamic-Pituitary-Adrenal axis; HPG axis – Hypothalamic-Pituitary-Gonad axis
This comprehensive review explores the intricate relationship between white matter (WM) asymmetry in the brain and handedness, shedding light on the intricate neural substrates that underlie manual dominance. The human brain's WM tracts, which facilitate rapid interregional communication, have been subjected to extensive investigation in the context of handedness. Diverse WM pathways, including commissural and association fibers, as well as global WM, have been implicated, although findings have often been contradictory. Studies utilizing diffusion tensor imaging (DTI) to assess WM asymmetry have revealed intriguing patterns by measuring anisotropy. Anisotropy is a measure of uniformity of axon directionality and diameter in WM tracts and is a useful measure of connectivity. Notably, right-handed individuals have demonstrated greater anisotropy, in both hemispheres, particularly in regions like the inferior frontal gyrus. Conversely, leftward fractional anisotropy (FA) asymmetry has been observed in left-handed individuals. Sex and age have also been identified as influential factors. Age, for instance, exhibits a positive association with FA in various WM tracts. Research on the corpus callosum has indicated differences between handedness groups, with left-handed twins displaying unique callosal patterns. Furthermore, studies have explored the corticospinal tract (CST) and its potential link to handedness. While some findings suggest CST structural asymmetry plays a role, others remain inconclusive. In conclusion, the interplay between WM asymmetry and handedness remains a complex and multifaceted phenomenon, influenced by various factors, including sex and age. Understanding these neural intricacies promises valuable insights into the nature of manual dominance and its broader implications for brain function. Abbreviations: AF - Arcuate Fasciculus; aGMD - Apparent Gray Matter Density; CC - Corpus Callosum; CSF - Cerebrospinal Fluid; CST - Corticospinal Tract; DGM - Deep Gray Matter; DTI - Diffusion Tensor Imaging; FA - Fractional Anisotropy; FDR - False Discovery Rate; GM - Gray Matter; GWAS - Genome Wide Association Studies; IHTT - Interhemispheric Transfer Time; IQ - Intelligence Quotient; IT - Interhemispheric Transfer; M1 - Primary Motor Cortex; MEP - Motor Evoked Potential; MRI - Magnetic Resonance Imaging; MTG - Medial Temporal Gyrus; NHP - Non-Human Primates; NRH - Non-right-handedness; SBA - Surface-Based Analysis; SNP - Single Nucleotide Polymorphism; SWI - Susceptibility-Weighted Imaging; TEA - Term Equivalent Age; TMS - Transcranial Magnetic Stimulation; VBM - Voxel-Based Morphometry; WM - White Matter
