Gender and cytoarchitecture differences: Functional connectivity of the hippocampal sub-regions.

Introduction: The hippocampus plays a significant role in learning, memory encoding, and spatial navigation. Typically, the hippocampus is investigated as a whole region of interest. However, recent work has developed fully detailed atlases based on cytoarchitecture properties of brain regions, and the hippocampus has been sub-divided into seven sub-areas that have structural differences in terms of distinct numbers of cells, neurons, and other structural and chemical properties. Moreover, gender differences are of increasing concern in neuroscience research. Several neuroscience studies have found structural and functional variations between the brain regions of females and males, and the hippocampus is one of these regions. Aim: The aim of this study to explore whether the cytoarchitecturally distinct sub-regions of the hippocampus have varying patterns of functional connectivity with different networks of the brain and how these functional connections differ in terms of gender differences. Method: This study investigated 200 healthy participants using seed-based resting-state functional magnetic resonance imaging (rsfMRI). The primary aim of this study was to explore the resting connectivity and gender distinctions associated with specific sub-regions of the hippocampus and their relationship with major functional brain networks. Results: The findings revealed that the majority of the seven hippocampal sub-regions displayed functional connections with key brain networks, and distinct patterns of functional connectivity were observed between the hippocampal sub-regions and various functional networks within the brain. Notably, the default and visual networks exhibited the most consistent functional connections. Additionally, gender-based analysis highlighted evident functional resemblances and disparities, particularly concerning the anterior section of the hippocampus. Conclusion: This study highlighted the functional connectivity patterns and involvement of the hippocampal sub-regions in major brain functional networks, indicating that the hippocampus should be investigated as a region of multiple distinct functions and should always be examined as sub-regions of interest. The results also revealed clear gender differences in functional connectivity.

An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI.

Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been shown to positively affect patients' lives. To save time and costs associated with clinical investigation, deep learning approaches have been used widely to predict MCI. This study proposes optimized deep learning models for differentiating between MCI and normal control samples. In previous studies, the hippocampus region located in the brain is used extensively to diagnose MCI. The entorhinal cortex is a promising area for diagnosing MCI since severe atrophy is observed when diagnosing the disease before the shrinkage of the hippocampus. Due to the small size of the entorhinal cortex area relative to the hippocampus, limited research has been conducted on the entorhinal cortex brain region for predicting MCI. This study involves the construction of a dataset containing only the entorhinal cortex area to implement the classification system. To extract the features of the entorhinal cortex area, three different neural network architectures are optimized independently: VGG16, Inception-V3, and ResNet50. The best outcomes were achieved utilizing the convolution neural network classifier and the Inception-V3 architecture for feature extraction, with accuracy, sensitivity, specificity, and area under the curve scores of 70%, 90%, 54%, and 69%, respectively. Furthermore, the model has an acceptable balance between precision and recall, achieving an F1 score of 73%. The results of this study validate the effectiveness of our approach in predicting MCI and may contribute to diagnosing MCI through MRI.

Detecting Cortical Thickness Changes in Epileptogenic Lesions Using Machine Learning.

Epilepsy is a neurological disorder characterized by abnormal brain activity. Epileptic patients suffer from unpredictable seizures, which may cause a loss of awareness. Seizures are considered drug resistant if treatment does not affect success. This leads practitioners to calculate the cortical thickness to measure the distance between the brain's white and grey matter surfaces at various locations to perform a surgical intervention. In this study, we introduce using machine learning as an approach to classify extracted measurements from T1-weighted magnetic resonance imaging. Data were collected from the epilepsy unit at King Abdulaziz University Hospital. We applied two trials to classify the extracted measurements from T1-weighted MRI for drug-resistant epilepsy and healthy control subjects. The preprocessing sequence on T1-weighted MRI images was performed using C++ through BrainSuite's pipeline. The first trial was performed on seven different combinations of four commonly selected measurements. The best performance was achieved in Exp6 and Exp7, with 80.00% accuracy, 83.00% recall score, and 83.88% precision. It is noticeable that grey matter volume and white matter volume measurements are more significant than the cortical thickness measurement. The second trial applied four different machine learning classifiers after applying 10-fold cross-validation and principal component analysis on all extracted measurements as in the first trial based on the mentioned previous works. The K-nearest neighbours model outperformed the other machine learning classifiers with 97.11% accuracy, 75.00% recall score, and 75.00% precision.

BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension.

A few methods and tools are available for the quantitative measurement of the brain volume targeting mainly brain volume loss. However, several factors, such as the clinical conditions, the time of the day, the type of MRI machine, the brain volume artifacts, the pseudoatrophy, and the variations among the protocols, produce extreme variations leading to misdiagnosis of brain atrophy. While brain white matter loss is a characteristic lesion during neurodegeneration, the main objective of this study was to create a computational tool for high precision measuring structural brain changes using the fractal dimension (FD) definition. The validation of the BrainFD software is based on T1-weighted MRI images from the Open Access Series of Imaging Studies (OASIS)-3 brain database, where each participant has multiple MRI scan sessions. The software is based on the Python and JAVA programming languages with the main functionality of the FD calculation using the box-counting algorithm, for different subjects on the same brain regions, with high accuracy and resolution, offering the ability to compare brain data regions from different subjects and on multiple sessions, creating different imaging profiles based on the Clinical Dementia Rating (CDR) scores of the participants. Two experiments were executed. The first was a cross-sectional study where the data were separated into two CDR classes. In the second experiment, a model on multiple heterogeneous data was trained, and the FD calculation for each participant of the OASIS-3 database through multiple sessions was evaluated. The results suggest that the FD variation efficiently describes the structural complexity of the brain and the related cognitive decline. Additionally, the FD efficiently discriminates the two classes achieving 100% accuracy. It is shown that this classification outperforms the currently existing methods in terms of accuracy and the size of the dataset. Therefore, the FD calculation for identifying intracranial brain volume loss could be applied as a potential low-cost personalized imaging biomarker. Furthermore, the possibilities measuring different brain areas and subregions could give robust evidence of the slightest variations to imaging data obtained from repetitive measurements to Physicians and Radiologists.

Developing fMRI protocol for clinical use Comparison of 6 Arabic paradigms for brain language mapping in native Arabic speakers.

OBJECTIVES: To assess a baseline assessment using developed functional magnetic resonance imaging (fMRI) language paradigms for Arabic-speakers. METHODS: 24-healthy right-handed volunteers scanned on a 3.0 Tesla MRI machine. For fMRI, a BOLD-sensitive sequence used to measure signals over time across 6 language paradigms: rhyming (RH), semantic category generations (SCG), silent word generation (SWG), verb generation picture (VGp), verb generation word (VGw), and verb generation audio (VGa). fMRI data was analyzed using FMRIB Software Library (FSL). RESULTS: We found that VGa, SWG, VGw and VGp robustly activated language-related regions in the dominant hemisphere. RH and SCG failed to adequately define these activation regions but this may be related to the study's preliminary nature and limitations. After assessment of their validity, considerable activation of the inferior frontal gyrus during VGa, SWG, VGw and VGp suggests that these paradigms have the potential for localizing of Broca's area in native Arabic speakers. CONCLUSION: Set of well adapted, and evidence-based, fMRI paradigms were established for Arabic-speakers to enable accurate and sufficient localization and lateralization of the language area. After validation, these paradigms may provide sequences for accurate localization of brain language areas, and could be used as a presurgical evaluation tool.

Abnormal Connectivity and Brain Structure in Patients With Visual Snow.

OBJECTIVE: Visual snow (VS) is a distressing, life-impacting condition with persistent visual phenomena. VS patients show cerebral hypermetabolism within the visual cortex, resulting in altered neuronal excitability. We hypothesized to see disease-dependent alterations in functional connectivity and gray matter volume (GMV) in regions associated with visual perception. METHODS: Nineteen patients with VS and 16 sex- and age-matched controls were recruited. Functional magnetic resonance imaging (fMRI) was applied to examine resting-state functional connectivity (rsFC). Volume changes were assessed by means of voxel-based morphometry (VBM). Finally, we assessed associations between MRI indices and clinical parameters. RESULTS: Patients with VS showed hyperconnectivity between extrastriate visual and inferior temporal brain regions and also between prefrontal and parietal (angular cortex) brain regions (p < 0.05, corrected for age and migraine occurrence). In addition, patients showed increased GMV in the right lingual gyrus (p < 0.05 corrected). Symptom duration positively correlated with GMV in both lingual gyri (p < 0.01 corrected). CONCLUSION: This study found VS to be associated with both functional and structural changes in the early and higher visual cortex, as well as the temporal cortex. These brain regions are involved in visual processing, memory, spatial attention, and cognitive control. We conclude that VS is not just confined to the visual system and that both functional and structural changes arise in VS patients, be it as an epiphenomenon or a direct contributor to the pathomechanism of VS. These in vivo neuroimaging biomarkers may hold potential as objective outcome measures of this so far purely subjective condition.

Volumetric and Shape Analysis of the Subcortical Regions in Schizophrenia Patients: A Pilot Study.

OBJECTIVE: Investigation of brain structure in disease has been enhanced by developments in shape analysis methods that can identify subtle regional surface distortions. High-resolution magnetic resonance (MR) imaging was used to compare volumetric and shape analysis in schizophrenia (SCZ) patients and healthy controls (CON). METHODS: T1-weighted, 1-mm thick MR images were acquired for 15 patients with SCZ and 15 age-matched healthy controls using subcortical volume and shape analysis, which we believe to be complimentary to volumetric measures. RESULTS: SCZ patients showed significant shape differences compared to healthy controls in the right hippocampus (P < 0.005), left and right putamen (P < 0.044 and P < 0.031), left caudate (P < 0.029), right pallidum (P < 0.019), and left thalamus (P < 0.033). CONCLUSION: Our results provide evidence for subcortical neuroanatomical changes in patients with SCZ. Hence, shape analysis may aid in the identification of structural biomarkers for identifying individuals of SCZ.

Structural alterations in brainstem of fibromyalgia syndrome patients correlate with sensitivity to mechanical pressure.

Fibromyalgia syndrome is a chronic pain disorder characterised by widespread pain and tenderness in muscles and deep tissues. Current theories regarding the pathophysiological origins of fibromyalgia syndrome point towards central sensitisation and a decreased capacity of descending nociceptive controls. Morphological alterations to subcortical brain regions may contribute to such pathophysiological mechanisms, and to pain and other symptoms seen in fibromyalgia. Therefore, we evaluated geometric differences in subcortical structures in fibromyalgia patients relative to healthy people using a novel method of shape analysis. Sixteen female fibromyalgia patients and 15 age and sex matched, healthy control subjects underwent high-resolution T1-weighted magnetic resonance image scanning. Data was analysed using shape analysis of 15 subcortical regions and standard voxel-based morphometry analysis. Fibromyalgia syndrome patients, relative to healthy control participants, exhibited alterations to the shape of the left lateral aspect of the lower brainstem (medulla). The mean total volume of the brainstem was also found to be significantly reduced in the patient group compared to healthy control subjects, and this brainstem volume reduction in patient group significantly correlated with clinical manual tender point scale scores. Voxel-based morphometry analysis revealed that patients also demonstrated decreased local grey matter volumes in the brainstem (pons) and left precuneus, and increased grey matter volumes in bilateral primary somatosensory cortices. Results suggest that the volume reduction and associated geometric shape alterations seen in the brainstem of the patient group may contribute to sensitivity to pressure pain in fibromyalgia syndrome. This finding may be due to structure-related deficiencies in regions subserving descending nociceptive control.

Neuroanatomical correlates of tinnitus revealed by cortical thickness analysis and diffusion tensor imaging.

INTRODUCTION: Tinnitus is a poorly understood auditory perception of sound in the absence of external stimuli. Convergent evidence proposes that tinnitus perception involves brain structural alterations as part of its pathophysiology. The aim of this study is to investigate the structural brain changes that might be associated with tinnitus-related stress and negative emotions. METHODS: Using high-resolution magnetic resonance imaging and diffusion tensor imaging, we investigated grey matter and white matter (WM) alterations by estimating cortical thickness measures, fractional anisotropy and mean diffusivity in 14 tinnitus subjects and 14 age- and sex-matched non-tinnitus subjects. RESULTS: Significant cortical thickness reductions were found in the prefrontal cortex (PFC), temporal lobe and limbic system in tinnitus subjects compared to non-tinnitus subjects. Tinnitus sufferers were found to have disrupted WM integrity in tracts involving connectivity of the PFC, temporal lobe, thalamus and limbic system. CONCLUSION: Our results suggest that such neural changes may represent neural origins for tinnitus or consequences of tinnitus and its associations.

Regional brain responses to pleasant and unpleasant IAPS pictures: different networks.

PURPOSE: The purpose of this study was to investigate the brain circuitry involved in the processing of both positive and negative emotions in normal healthy subjects. METHOD: we have recruited 15 healthy volunteers (9 males and 6 females, age range 30-60). In this block-design fMRI study, we compared the blood oxygen level dependant (BOLD) signal change as response to pleasant and unpleasant IAPS pictures, each compared to a neutral condition. RESULTS: Pleasant pictures versus neutral condition contrast demonstrated significant activation (p(FDRcorrected) <0.05) in bilateral pre-frontal cortex (PFC), anterior and posterior cingulate gyri and temporal lobe. Unpleasant pictures relative to neutral condition exhibit significant activation (p(FDRcorrected) <0.05) in amygdala, hippocampus, parahippocampal gyri, temporal lobe, visual cortex, fusiform gyri, PFC and anterior cingulate gyrus. CONCLUSION: Amygdala is mainly involved in the processing of negative emotions. Although an overlap in regions involved in the processing of pleasant and unpleasant IAPS pictures exists, the neural network for each is unique.

Cortical activation changes during repeated laser stimulation: a magnetoencephalographic study.

Repeated warm laser stimuli produce a progressive increase of the sensation of warmth and heat and eventually that of a burning pain. The pain resulting from repetitive warm stimuli is mediated by summated C fibre responses. To shed more light on the cortical changes associated with pain during repeated subnoxious warm stimulation, we analysed magnetoencephalographic (MEG) evoked fields in eleven subjects during application of repetitive warm laser stimuli to the dorsum of the right hand. One set of stimuli encompassed 10 laser pulses occurring at 2.5 s intervals. Parameters of laser stimulation were optimised to elicit a pleasant warm sensation upon a single stimulus with a rise of skin temperature after repeated stimulation not exceeding the threshold of C mechano-heat fibres. Subjects reported a progressive increase of the intensity of heat and burning pain during repeated laser stimulation in spite of only mild (4.8°C) increase of skin temperature from the first stimulus to the tenth stimulus. The mean reaction time, evaluated in six subjects, was 1.33 s, confirming involvement of C fibres. The neuromagnetic fields were modelled by five equivalent source dipoles located in the occipital cortex, cerebellum, posterior cingulate cortex, and left and right operculo-insular cortex. The only component showing statistically significant changes during repetitive laser stimulation was the late component of the contralateral operculo-insular source peaking at 1.05 s after stimulus onset. The amplitude increases of the late component of the contralateral operculo-insular source dipole correlated with the subjects' numerical ratings of warmth and pain. Results point to a pivotal role of the contralateral operculo-insular region in processing of C-fibre mediated pain during repeated subnoxious laser stimulation.

Plasticity of the superior and middle cerebellar peduncles in musicians revealed by quantitative analysis of volume and number of streamlines based on diffusion tensor tractography.

This work was conducted to study the plasticity of superior (SCP) and middle (MCP) cerebellar peduncles in musicians. The cerebellum is well known to support several musically relevant motor, sensory and cognitive functions. Previous studies reported increased cerebellar volume and grey matter (GM) density in musicians. Here, we report on plasticity of white matter (WM) of the cerebellum. Our cohort included 10/10 gender and handedness-matched musicians and controls. Using diffusion tensor imaging, fibre tractography of SCP and MCP was performed. The fractional anisotropy (FA), number of streamlines and volume of streamlines of SCP/MCP were compared between groups. Automatic measurements of GM and WM volumes of the right/left cerebellar hemispheres were also compared. Musicians have significantly increased right SCP volume (p = 0.02) and number of streamlines (p = 0.001), right MCP volume (p = 0.004) and total WM volume of the right cerebellum (p = 0.003). There were no significant differences in right MCP number of streamlines, left SCP/MCP volume and number of streamlines, SCP/MCP FA values, GM volume of the right cerebellum and GM/WM volumes of the left cerebellum. We propose that increased volume and number of streamlines of the right cerebellar peduncles represent use-dependent structural adaptation to increased sensorimotor and cognitive functional demands on the musician's cerebellum.