A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images

Lateral septum as a possible regulatory center of maternal behaviors

The lateral septum (LS) is involved in controlling anxiety, aggression, feeding, and other motivated behaviors. Lesion studies have also implicated the LS in various forms of caring behaviors. Recently, novel experimental tools have provided a more detailed insight into the function of the LS, including the specific role of distinct cell types and their neuronal connections in behavioral regulations, in which the LS participates. This article discusses the regulation of different types of maternal behavioral alterations using the distributions of established maternal hormones such as prolactin, estrogens, and the neuropeptide oxytocin. It also considers the distribution of neurons activated in mothers in response to pups and other maternal activities, as well as gene expressional alterations in the maternal LS. Finally, this paper proposes further research directions to keep up with the rapidly developing knowledge on maternal behavioral control in other maternal brain regions.

Multi-shell diffusion MRI of the fornix as a biomarker for cognition in Alzheimer's disease

BACKGROUND AND PURPOSE

A substantial fraction of those who had Alzheimer's Disease (AD) pathology on autopsy did not have dementia in life. While biomarkers for AD pathology are well-developed, biomarkers specific to cognitive domains affected by early AD are lagging. Diffusion MRI (dMRI) of the fornix is a candidate biomarker for early AD-related cognitive changes but is susceptible to bias due to partial volume averaging (PVA) with cerebrospinal fluid. The purpose of this work is to leverage multi-shell dMRI to correct for PVA and to evaluate PVA-corrected dMRI measures in fornix as a biomarker for cognition in AD.

METHODS

Thirty-three participants in the Cleveland Alzheimer's Disease Research Center (CADRC) (19 with normal cognition (NC), 10 with mild cognitive impairment (MCI), 4 with dementia due to AD) were enrolled in this study. Multi-shell dMRI was acquired, and voxelwise fits were performed with two models: 1) diffusion tensor imaging (DTI) that was corrected for PVA and 2) neurite orientation dispersion and density imaging (NODDI). Values of tissue integrity in fornix were correlated with neuropsychological scores taken from the Uniform Data Set (UDS), including the UDS Global Composite 5 score (UDSGC5).

RESULTS

Statistically significant correlations were found between the UDSGC5 and PVA-corrected measure of mean diffusivity (MDc, r = -0.35, p < 0.05) from DTI and the intracelluar volume fraction (ficvf, r = 0.37, p < 0.04) from NODDI. A sensitivity analysis showed that the relationship to MDc was driven by episodic memory, which is often affected early in AD, and language.

CONCLUSION

This cross-sectional study suggests that multi-shell dMRI of the fornix that has been corrected for PVA is a potential biomarker for early cognitive domain changes in AD. A longitudinal study will be necessary to determine if the imaging measure can predict cognitive decline.

Structural and functional disruption of subcortical limbic structures related with executive function in pediatric bipolar disorder

BACKGROUND

Impaired cognition has been demonstrated in pediatric bipolar disorder (PBD). The subcortical limbic structures play a key role in PBD. However, alternations of anatomical and functional characteristics of subcortical limbic structures and their relationship with neurocognition of PBD remain unclear.

METHODS

Thirty-six PBD type I (PBD-I) (15.36 ± 0.32 years old), twenty PBD type II (PBD-II) (14.80 ± 0.32 years old) and nineteen age-gender matched healthy controls (HCs) (14.16 ± 0.36 years old) were enlisted. Primarily, the volumes of the subcortical limbic structures were obtained and differences in the volumes were evaluated. Then, these structures served as seeds of regions of interest to calculate the voxel-wised functional connectivity (FC). After that, correlation analysis was completed between volumes and FC of brain regions showing significant differences and neuropsychological tests.

RESULTS

Compared to HCs, both PBD-I and PBD-II patients showed a decrease in the Stroop color word test (SCWT) and digit span backward test scores. Compared with HCs, PBD-II patients exhibited a significantly increased volume of right septal nuclei, and PBD-I patients presented increased FC of right nucleus accumbens and bilateral pallidum, of right basal forebrain with right putamen and left pallidum. Both the significantly altered volumes and FC were negatively correlated with SCWT scores.

SIGNIFICANCE

The study revealed the role of subcortical limbic structural and functional abnormalities on cognitive impairments in PBD patients. These may have far-reaching significance for the etiology of PBD and provide neuroimaging clues for the differential diagnosis of PBD subtypes.

CONCLUSIONS

Distinctive features of neural structure and function in PBD subtypes may contribute to better comprehending the potential mechanisms of PBD.

Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders

BACKGROUND

Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics.

METHODS

We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV).

RESULTS

Limbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male-female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction.

CONCLUSIONS

Overall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.

Basal forebrain integrity, cholinergic innervation and cognition in idiopathic Parkinson's disease

Most individuals with Parkinson's disease experience cognitive decline. Mounting evidence suggests this is partially caused by cholinergic denervation due to α-synuclein pathology in the cholinergic basal forebrain. Alpha-synuclein deposition causes inflammation, which can be measured with free water fraction, a diffusion MRI-derived metric of extracellular water. Prior studies have shown an association between basal forebrain integrity and cognition, cholinergic levels and cognition, and basal forebrain volume and acetylcholine, but no study has directly investigated whether basal forebrain physiology mediates the relationship between acetylcholine and cognition in Parkinson's disease. We investigated the relationship between these variables in a cross-sectional analysis of 101 individuals with Parkinson's disease. Cholinergic levels were measured using fluorine-18 fluoroethoxybenzovesamicol (18F-FEOBV) PET imaging. Cholinergic innervation regions of interest included the medial, lateral capsular and lateral perisylvian regions and the hippocampus. Brain volume and free water fraction were quantified using T1 and diffusion MRI, respectively. Cognitive measures included composites of attention/working memory, executive function, immediate memory and delayed memory. Data were entered into parallel mediation analyses with the cholinergic projection areas as predictors, cholinergic basal forebrain volume and free water fraction as mediators and each cognitive domain as outcomes. All mediation analyses controlled for age, years of education, levodopa equivalency dose and systolic blood pressure. The basal forebrain integrity metrics fully mediated the relationship between lateral capsular and lateral perisylvian acetylcholine and attention/working memory, and partially mediated the relationship between medial acetylcholine and attention/working memory. Basal forebrain integrity metrics fully mediated the relationship between medial, lateral capsular and lateral perisylvian acetylcholine and free water fraction. For all mediations in attention/working memory and executive function, the free water mediation was significant, while the volume mediation was not. The basal forebrain integrity metrics fully mediated the relationship between hippocampal acetylcholine and delayed memory and partially mediated the relationship between lateral capsular and lateral perisylvian acetylcholine and delayed memory. The volume mediation was significant for the hippocampal and lateral perisylvian models, while free water fraction was not. Free water fraction in the cholinergic basal forebrain mediated the relationship between acetylcholine and attention/working memory and executive function, while cholinergic basal forebrain volume mediated the relationship between acetylcholine in temporal regions in memory. These findings suggest that these two metrics reflect different stages of neurodegenerative processes and add additional evidence for a relationship between pathology in the basal forebrain, acetylcholine denervation and cognitive decline in Parkinson's disease.

Opposing effects of thyroid hormones on hypothalamic subunits and limbic structures in hyperthyroidism patients: A comprehensive volumetric study

Thyroid hormones play a critical role in brain development, but paradoxically, patients with hyperthyroidism often exhibit cognitive decline and irritability. This study aims to explore the pattern of atrophy in hyperthyroid patients, changes in specific areas of the brain, including hypothalamic subfields and limbic structures, and their relationships with hormonal levels and psychometric tests. This prospective cross-sectional study involves 19 newly diagnosed, untreated hyperthyroid patients, and 15 age and gender-matched control subjects. The participants underwent psychometric and cognitive tests and volumetric MRI. The hypothalamic subfield (anterior-inferior, anterior-superior, superior-tubular, inferior-tubular, and posterior hypothalamus) and limbic structures (fornix, basal forebrain, nucleus accumbens, and septal nucleus) were segmented using voxel-based morphometry, surface-based morphometry, and deep learning algorithms. The groups were compared using the t-test, and correlation analyses were performed between clinical parameters and volumetric measurements. The correlation between hormonal parameters and volumetric measurements in patient and control groups was assessed with the Meng test. Hyperthyroid patients displayed widespread grey matter loss and sulcal shallowing in the left hemisphere. However, no local gyrification index changes or cortical thickness variations were detected. The limbic structures and hypothalamic subunits did not show any volume discrepancies. Free thyroxine in the patient group negatively correlated with bilateral anterior-inferior and right septal nucleus, but positively correlated with left anterior-inferior in the control group. Thyroid stimulating hormone in the patient group showed a positive correlation with bilateral fornix volume, a correlation absent in the control group. Disease duration negatively correlated with right anterior-inferior, right tubular inferior, and right septal nucleus. Changes in cognitive and psychometric test scores in the patient group correlated with the bilateral septal nucleus volume. Hyperthyroidism primarily leads to a reduction in grey matter volume and sulcal shallowing. Thyroid hormones have differing volumetric effects in limbic structures and hypothalamic subunits under physiological and hyperthyroid conditions.

Cholinergic basal forebrain system degeneration underlies postural instability/gait difficulty and attention impairment in Parkinson's disease

BACKGROUND AND PURPOSE

The specific pathophysiological mechanisms underlying postural instability/gait difficulty (PIGD) and cognitive function in Parkinson's disease (PD) remain unclear. Both postural and gait control, as well as cognitive function, are associated with the cholinergic basal forebrain (cBF) system.

METHODS

A total of 84 PD patients and 82 normal controls were enrolled. Each participant underwent motor and cognitive assessments. Diffusion tensor imaging was used to detect structural abnormalities in the cBF system. The cBF was segmented using FreeSurfer, and its fiber tract was traced using probabilistic tractography. To provide information on extracellular water accumulation, free-water fraction (FWf) was quantified. FWf in the cBF and its fiber tract, as well as cortical projection density, were extracted for statistical analyses.

RESULTS

Patients had significantly higher FWf in the cBF (p < 0.001) and fiber tract (p = 0.021) than normal controls, as well as significantly lower cBF projection in the occipital (p < 0.001), parietal (p < 0.001) and prefrontal cortex (p = 0.005). In patients, a higher FWf in the cBF correlated with worse PIGD score (r = 0.306, p = 0.006) and longer Trail Making Test A time (r = 0.303, p = 0.007). Attentional function (Trail Making Test A) partially mediated the association between FWf in the cBF and PIGD score (indirect effect, a*b = 0.071; total effect, c = 0.256; p = 0.006).

CONCLUSIONS

Our findings suggest that degeneration of the cBF system in PD, from the cBF to its fiber tract and cortical projection, plays an important role in cognitive-motor interaction.

Toward hippocampal volume measures on ultra-high field magnetic resonance imaging: a comprehensive comparison study between deep learning and conventional approaches

The hippocampus is a complex brain structure that plays an important role in various cognitive aspects such as memory, intelligence, executive function, and path integration. The volume of this highly plastic structure is identified as one of the most important biomarkers of specific neuropsychiatric and neurodegenerative diseases. It has also been extensively investigated in numerous aging studies. However, recent studies on aging show that the performance of conventional approaches in measuring the hippocampal volume is still far from satisfactory, especially in terms of delivering longitudinal measures from ultra-high field magnetic resonance images (MRIs), which can visualize more boundary details. The advancement of deep learning provides an alternative solution to measuring the hippocampal volume. In this work, we comprehensively compared a deep learning pipeline based on nnU-Net with several conventional approaches including Freesurfer, FSL and DARTEL, for automatically delivering hippocampal volumes: (1) Firstly, we evaluated the segmentation accuracy and precision on a public dataset through cross-validation. Results showed that the deep learning pipeline had the lowest mean (L = 1.5%, R = 1.7%) and the lowest standard deviation (L = 5.2%, R = 6.2%) in terms of volume percentage error. (2) Secondly, sub-millimeter MRIs of a group of healthy adults with test-retest 3T and 7T sessions were used to extensively assess the test-retest reliability. Results showed that the deep learning pipeline achieved very high intraclass correlation coefficients (L = 0.990, R = 0.986 for 7T; L = 0.985, R = 0.983 for 3T) and very small volume percentage differences (L = 1.2%, R = 0.9% for 7T; L = 1.3%, R = 1.3% for 3T). (3) Thirdly, a Bayesian linear mixed effect model was constructed with respect to the hippocampal volumes of two healthy adult datasets with longitudinal 7T scans and one disease-related longitudinal dataset. It was found that the deep learning pipeline detected both the subtle and disease-related changes over time with high sensitivity as well as the mild differences across subjects. Comparison results from the aforementioned three aspects showed that the deep learning pipeline significantly outperformed the conventional approaches by large margins. Results also showed that the deep learning pipeline can better accommodate longitudinal analysis purposes.

Volume changes of the subcortical limbic structures in major depressive disorder patients with and without anhedonia

Anhedonia is a core feature of major depressive disorder (MDD) and the limbic system has been indicated to be associated with anhedonia in MDD due to its crucial role within the reward circuit. However, the relationship between different regions of the limbic system and MDD, particularly anhedonic symptoms, remains unclear. Therefore, the purpose of this study was to investigate volume changes of various parts of the subcortical limbic (ScLimbic) system in MDD with and without anhedonia. A total of 120 individuals, including 30 MDD patients with anhedonia, 43 MDD patients without anhedonia, and 47 healthy controls (HCs) were enrolled in this study. All subjects underwent structural magnetic resonance imaging scans. After that, ScLimbic system segmentation was performed using the FreeSurfer pipeline ScLimbic. Analysis of covariance (ANCOVA) was performed to identify brain regions with significant volume differences among three groups, and then, post hoc tests were calculated for inter-group comparisons. Finally, correlations between volumes of different parts of the ScLimbic and clinical characteristics in MDD patients were further analyzed. The ANCOVA revealed significant volume differences of the ScLimbic system among three groups in the bilateral fornix (Fx), and the right basal forebrain (BF). As compared with HCs, both groups of MDD patients showed decreased volume in the right Fx, meanwhile, MDD patients with anhedonia further exhibited volume reductions in the left Fx and right BF. However, no significant difference was found between MDD patients with and without anhedonia. No significant association was observed between subregion volumes of the ScLimbic system and clinical features in MDD. The present findings demonstrated that MDD patients with and without anhedonia exhibited segregated brain structural alterations in the ScLimbic system and volume loss of the ScLimbic system might be fairly extensive in MDD patients with anhedonia.

Deep Learning Segmentation of the Nucleus Basalis of Meynert on 3T MRI

BACKGROUND AND PURPOSE

The nucleus basalis of Meynert is a key subcortical structure that is important in arousal and cognition and has been explored as a deep brain stimulation target but is difficult to study due to its small size, variability among patients, and lack of contrast on 3T MR imaging. Thus, our goal was to establish and evaluate a deep learning network for automatic, accurate, and patient-specific segmentations with 3T MR imaging.

MATERIALS AND METHODS

Patient-specific segmentations can be produced manually; however, the nucleus basalis of Meynert is difficult to accurately segment on 3T MR imaging, with 7T being preferred. Thus, paired 3T and 7T MR imaging data sets of 21 healthy subjects were obtained. A test data set of 6 subjects was completely withheld. The nucleus was expertly segmented on 7T, providing accurate labels for the paired 3T MR imaging. An external data set of 14 patients with temporal lobe epilepsy was used to test the model on brains with neurologic disorders. A 3D-Unet convolutional neural network was constructed, and a 5-fold cross-validation was performed.

RESULTS

The novel segmentation model demonstrated significantly improved Dice coefficients over the standard probabilistic atlas for both healthy subjects (mean, 0.68 [SD, 0.10] versus 0.45 [SD, 0.11], P = .002, t test) and patients (0.64 [SD, 0.10] versus 0.37 [SD, 0.22], P < .001). Additionally, the model demonstrated significantly decreased centroid distance in patients (1.18 [SD, 0.43] mm, 3.09 [SD, 2.56] mm, P = .007).

CONCLUSIONS

We developed the first model, to our knowledge, for automatic and accurate patient-specific segmentation of the nucleus basalis of Meynert. This model may enable further study into the nucleus, impacting new treatments such as deep brain stimulation.

Alterations of Limbic Structure Volumes in Patients with Obstructive Sleep Apnea

OBJECTIVES

We investigated the change in limbic structure volumes and intrinsic limbic network in patients with obstructive sleep apnea (OSA) compared to healthy controls.

METHODS

We enrolled 26 patients with OSA and 30 healthy controls. They underwent three-dimensional T1-weighted magnetic resonance imaging (MRI) on a 3 T MRI scanner. The limbic structures were analyzed volumetrically using the FreeSurfer program. We examined the intrinsic limbic network using the Brain Analysis with Graph Theory program and compared the groups' limbic structure volumes and intrinsic limbic network.

RESULTS

There were significant differences in specific limbic structure volumes between the groups. The volumes in the right amygdala, right hippocampus, right hypothalamus, right nucleus accumbens, left amygdala, left basal forebrain, left hippocampus, left hypothalamus, and left nucleus accumbens in patients with OSA were lower than those in healthy controls (right amygdala, 0.102 vs. 0.113%, p = 0.004; right hippocampus, 0.253 vs. 0.281%, p = 0.002; right hypothalamus, 0.028 vs. 0.032%, p = 0.002; right nucleus accumbens, 0.021 vs. 0.024%, p = 0.019; left amygdala, 0.089 vs. 0.098%, p = 0.007; left basal forebrain, 0.020 vs. 0.022%, p = 0.027; left hippocampus, 0.245 vs. 0.265%, p = 0.021; left hypothalamus, 0.028 vs. 0.031%, p = 0.016; left nucleus accumbens, 0.023 vs. 0.027%, p = 0.002). However, there were no significant differences in network measures between the groups.

CONCLUSION

We demonstrate that the volumes of several limbic structures in patients with OSA are significantly lower than those in healthy controls. However, there are no alterations to the intrinsic limbic network. These findings suggest that OSA is one of the risk factors for cognitive impairments.

Memory Phenotypes In Early, De Novo Parkinson's Disease Patients with Mild Cognitive Impairment

BACKGROUND

Memory deficits in mild cognitive impairment related to Parkinson's disease (PD-MCI) are quite heterogeneous, and there is no general agreement on their genesis.

OBJECTIVES

To define memory phenotypes in de novo PD-MCI and their associations with motor and non-motor features and patients' quality of life.

METHODS

From a sample of 183 early de novo patients with PD, cluster analysis was applied to neuropsychological measures of memory function of 82 patients with PD-MCI (44.8%). The remaining patients free of cognitive impairment were considered as a comparison group (n = 101). Cognitive measures and structural magnetic resonance imaging-based neural correlates of memory function were used to substantiate the results.

RESULTS

A three-cluster model produced the best solution. Cluster A (65.85%) included memory unimpaired patients; Cluster B (23.17%) included patients with mild episodic memory disorder related to a "prefrontal executive-dependent phenotype"; Cluster C (10.97%) included patients with severe episodic memory disorder related to a "hybrid phenotype," where hippocampal-dependent deficits co-occurred with prefrontal executive-dependent memory dysfunctions. Cognitive and brain structural imaging correlates substantiated the findings. The three phenotypes did not differ in terms of motor and non-motor features, but the attention/executive deficits progressively increased from Cluster A, through Cluster B, to Cluster C. This last cluster had worse quality of life compared to others.

CONCLUSIONS

Our results demonstrated the memory heterogeneity of de novo PD-MCI, suggesting existence of three distinct memory-related phenotypes. Identification of such phenotypes can be fruitful in understanding the pathophysiological mechanisms underlying PD-MCI and its subtypes and in guiding appropriate treatments. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Brain networks and epilepsy development in patients with Alzheimer disease

INTRODUCTION

This study aimed to investigate the association between brain networks and epilepsy development in patients with Alzheimer disease (AD).

METHODS

We enrolled patients newly diagnosed with AD at our hospital who underwent three-dimensional T1-weighted magnetic resonance imaging at the time of AD diagnosis and included healthy controls. We obtained the cortical, subcortical, and thalamic nuclei structural volumes using FreeSurfer and applied graph theory to obtain the global brain network and intrinsic thalamic network based on the structural volumes using BRAPH.

RESULTS

We enrolled 25 and 56 patients with AD with and without epilepsy development, respectively. We also included 45 healthy controls. The global brain network differed between the patients with AD and healthy controls. The local efficiency (2.026 vs. 3.185, p = .048) and mean clustering coefficient (0.449 vs. 1.321, p = .024) were lower, whereas the characteristic path length (0.449 vs. 1.321, p = .048) was higher in patients with AD than in healthy controls. Both global and intrinsic thalamic networks were significantly different between AD patients with and without epilepsy development. In the global brain network, local efficiency (1.340 vs. 2.401, p = .045), mean clustering coefficient (0.314 vs. 0.491, p = .045), average degree (27.442 vs. 41.173, p = .045), and assortative coefficient (-0.041 vs. -0.011, p = .045) were lower, whereas the characteristic path length (2.930 vs. 2.118, p = .045) was higher in patients with AD with epilepsy development than in those without. In the intrinsic thalamic network, the mean clustering coefficient (0.646 vs. 0.460, p = .048) was higher, whereas the characteristic path length (1.645 vs. 2.232, p = .048) was lower in patients with AD with epilepsy development than in those without.

CONCLUSION

We found that the global brain network differs between patients with AD and healthy controls. In addition, we demonstrated significant associations between brain networks (both global brain and intrinsic thalamic networks) and epilepsy development in patients with AD.

Priorities for research on neuromodulatory subcortical systems in Alzheimer's disease: Position paper from the NSS PIA of ISTAART

The neuromodulatory subcortical system (NSS) nuclei are critical hubs for survival, hedonic tone, and homeostasis. Tau-associated NSS degeneration occurs early in Alzheimer's disease (AD) pathogenesis, long before the emergence of pathognomonic memory dysfunction and cortical lesions. Accumulating evidence supports the role of NSS dysfunction and degeneration in the behavioral and neuropsychiatric manifestations featured early in AD. Experimental studies even suggest that AD-associated NSS degeneration drives brain neuroinflammatory status and contributes to disease progression, including the exacerbation of cortical lesions. Given the important pathophysiologic and etiologic roles that involve the NSS in early AD stages, there is an urgent need to expand our understanding of the mechanisms underlying NSS vulnerability and more precisely detail the clinical progression of NSS changes in AD. Here, the NSS Professional Interest Area of the International Society to Advance Alzheimer's Research and Treatment highlights knowledge gaps about NSS within AD and provides recommendations for priorities specific to clinical research, biomarker development, modeling, and intervention. HIGHLIGHTS: Neuromodulatory nuclei degenerate in early Alzheimer's disease pathological stages. Alzheimer's pathophysiology is exacerbated by neuromodulatory nuclei degeneration. Neuromodulatory nuclei degeneration drives neuropsychiatric symptoms in dementia. Biomarkers of neuromodulatory integrity would be value-creating for dementia care. Neuromodulatory nuclei present strategic prospects for disease-modifying therapies.

Involvement of limbic structures in patients with isolated rapid eye movement sleep behavior disorder

This study aimed to investigate the alterations in limbic structure volumes and limbic covariance network in patients with isolated rapid eye movement (REM) sleep behavior disorder (iRBD) and to compare them with healthy controls. We retrospectively enrolled 35 patients with iRBD and 35 healthy controls who underwent three-dimensional T1-weighted brain MRI. Volumetric analysis of subcortical limbic structures, including the hippocampus, amygdala, thalamus, mammillary body, hypothalamus, basal forebrain, septal nuclei, fornix, and nucleus accumbens, was performed. Furthermore, the limbic covariance network was examined using graph theory based on the limbic structure volumes. Some of the limbic structure volumes differed significantly. The right amygdala and hypothalamus volumes were lower in the patients with iRBD than in the healthy controls (0.101% vs. 0.114%, p = 0.016, and 0.027% vs. 0.030%, p = 0.045, respectively). However, there were no significant differences in the limbic covariance network between the groups. This study demonstrated that the volumes of the right amygdala and hypothalamus are lower in patients with iRBD, even without cognitive impairments, than in healthy controls. However, there were no significant differences in the limbic covariance network between the groups. The involvements of the limbic structures could be related to the conversion to neurodegenerative diseases in patients with iRBD.

Effectiveness of ocrelizumab on clinical and MRI outcome measures in multiple sclerosis across black and white cohorts: A single-center retrospective study

OBJECTIVE

To examine differences in the therapeutic response to ocrelizumab in multiple sclerosis (MS) patients who self-identified as either White or Black, assessed longitudinally by expanded disability status scale (EDSS) progression and MRI brain volume loss.

METHODS

MS subjects treated with ocrelizumab were retrospectively identified. Clinical data were available for 229 subjects (White 146; Black 83) and MRI data from for 48 subjects (White 31; Black 17). Outcome measures were changes in the EDSS and brain volume over time. EDSS were analyzed as raw scores, ambulatory (EDSS <5.0) vs. ambulatory with assistance (5.5 ≤ EDSS ≤ 6.5) status, and EDSS severity (< 3.0, 3.0-5.0, and > 5.5 ≤ 6.5). General linear mixed model was used for statistical analysis. FreeSurfer was used for volumetric analysis.

RESULTS

The Black cohort had overrepresentation of females (78% vs. 62%, p = 0.013), lower age (median, 45 (IQR 39-51) vs. 49 (38-58), p = 0.08), lower Vitamin D levels (33 (21-45) vs. 40 (29-52), p = 0.002), and higher EDSS (4 (2-6) vs. 2.5 (1-6), p = 0.019). There was no progression of EDSS scores over the 2-year observation period. The covariates with significant influence on the baseline EDSS scores were older age, race, longer disease duration, prior MS treatment, and lower vitamin D levels. No differences were observed between the racial groups over time in the cortical, thalamic, caudate, putamen, and brainstem gray matter volumes nor in the cortical thickness or total lesion volume.

CONCLUSION

In this real-world clinical and radiological study, ocrelizumab treatment was highly effective in stabilizing clinical and MRI measures of disease progression in Blacks and Whites, despite higher baseline disability in the Black cohort.

SynthStrip: skull-stripping for any brain image

The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skull-stripping methods are usually tailored to images with specific acquisition properties, namely near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are prevalent in research settings. As a result, existing tools tend to adapt poorly to other image types, such as stacks of thick slices acquired with fast spin-echo (FSE) MRI that are common in the clinic. While learning-based approaches for brain extraction have gained traction in recent years, these methods face a similar burden, as they are only effective for image types seen during the training procedure. To achieve robust skull-stripping across a landscape of imaging protocols, we introduce SynthStrip, a rapid, learning-based brain-extraction tool. By leveraging anatomical segmentations to generate an entirely synthetic training dataset with anatomies, intensity distributions, and artifacts that far exceed the realistic range of medical images, SynthStrip learns to successfully generalize to a variety of real acquired brain images, removing the need for training data with target contrasts. We demonstrate the efficacy of SynthStrip for a diverse set of image acquisitions and resolutions across subject populations, ranging from newborn to adult. We show substantial improvements in accuracy over popular skull-stripping baselines - all with a single trained model. Our method and labeled evaluation data are available at https://w3id.org/synthstrip.

Inconsistencies in atlas-based volumetric measures of the human nucleus basalis of Meynert: A need for high-resolution alternatives

The nucleus basalis of Meynert (nbM) is the major source of cortical acetylcholine (ACh) and has been related to cognitive processes and to neurological disorders. However, spatially delineating the human nbM in MRI studies remains challenging. Due to the absence of a functional localiser for the human nbM, studies to date have localised it using nearby neuroanatomical landmarks or using probabilistic atlases. To understand the feasibility of MRI of the nbM we set our four goals; our first goal was to review current human nbM region-of-interest (ROI) selection protocols used in MRI studies, which we found have reported highly variable nbM volume estimates. Our next goal was to quantify and discuss the limitations of existing atlas-based volumetry of nbM. We found that the identified ROI volume depends heavily on the atlas used and on the probabilistic threshold set. In addition, we found large disparities even for data/studies using the same atlas and threshold. To test whether spatial resolution contributes to volume variability, as our third goal, we developed a novel nbM mask based on the normalized BigBrain dataset. We found that as long as the spatial resolution of the target data was 1.3 mm isotropic or above, our novel nbM mask offered realistic and stable volume estimates. Finally, as our last goal we tried to discern nbM using publicly available and novel high resolution structural MRI ex vivo MRI datasets. We find that, using an optimised 9.4T quantitative T₂^⁎ ex vivo dataset, the nbM can be visualised using MRI. We conclude caution is needed when applying the current methods of mapping nbM, especially for high resolution MRI data. Direct imaging of the nbM appears feasible and would eliminate the problems we identify, although further development is required to allow such imaging using standard (f)MRI scanning.

Analysis of the extent of limbic system changes in multiple sclerosis using FreeSurfer and voxel-based morphometry approaches

Background and purpose

The limbic brain is involved in diverse cognitive, emotional, and autonomic functions. Injury of the various parts of the limbic system have been correlated with clinical deficits in MS. The purpose of this study was to comprehensively examine different regions of the subcortical limbic system to assess the extent of damage within this entire system as it may be pertinent in correlating with specific aspects of cognitive and behavioral dysfunction in MS by using a fully automated, unbiased segmentation approach.

Methods

Sixty-seven subjects were included in this study, including 52 with multiple sclerosis (MS) and 15 healthy controls. Only patients with stable MS disease, without any relapses, MRI activity, or disability progression were included. Subcortical limbic system segmentation was performed using the FreeSurfer pipeline ScLimbic, which provides volumes for fornix, mammillary bodies, hypothalamus, septal nuclei, nucleus accumbens, and basal forebrain. Hippocampus and anterior thalamic nuclei were added as additional components of the limbic circuitry, also segmented through FreeSurfer. Whole limbic region mask was generated by combining these structures and used for Voxel-based morphometry (VBM) analysis.

Results

The mean [95% confidence interval] of the total limbic system volume was lower (0.22% [0.21–0.23]) in MS compared to healthy controls (0.27%, [0.25–0.29], p < .001). Pairwise comparisons of individual limbic regions between MS and controls was significant in the nucleus accumbens (0.046%, [0.043–0.050] vs. 0.059%, [0.051–0.066], p = .005), hypothalamus (0.062%, [0.059–0.065] vs. 0.074%, [0.068–0.081], p = .001), basal forebrain (0.038%, [0.036–0.040] vs. 0.047%, [0.042–0.051], p = .001), hippocampus (0.47%, [0.45–0.49] vs. 0.53%, [0.49–0.57], p = .004), and anterior thalamus (0.077%, [0.072–0.082] vs. 0.093%, [0.084–0.10], p = .001) after Bonferroni correction. Volume of several limbic regions was significantly correlated with T2 lesion burden and brain parenchymal fraction (BPF). Multiple regression model showed minimal influence of BPF on limbic brain volume and no influence of other demographic and disease state variables. VBM analysis showed cluster differences in the fornix and anterior thalamic nuclei at threshold p < 0.05 after adjusting for covariates but the results were insignificant after family-wise error corrections.

Conclusions

The results show evidence that brain volume loss is fairly extensive in the limbic brain. Given the significance of the limbic system in many disease states including MS, such volumetric analyses can be expanded to studying cognitive and emotional disturbances in larger clinical trials. FreeSurfer ScLimbic pipeline provided an efficient and reliable methodology for examining many of the subcortical structures related to the limbic brain.

High-resolution atlasing and segmentation of the subcortex: Review and perspective on challenges and opportunities created by machine learning

This paper reviews almost three decades of work on atlasing and segmentation methods for subcortical structures in human brain MRI. In writing this survey, we have three distinct aims. First, to document the evolution of digital subcortical atlases of the human brain, from the early MRI templates published in the nineties, to the complex multi-modal atlases at the subregion level that are available today. Second, to provide a detailed record of related efforts in the automated segmentation front, from earlier atlas-based methods to modern machine learning approaches. And third, to present a perspective on the future of high-resolution atlasing and segmentation of subcortical structures in in vivo human brain MRI, including open challenges and opportunities created by recent developments in machine learning.

Alterations of limbic structure volumes and limbic covariance network in patients with cluster headache

The aim of this study was to compare the limbic structures and covariance network in patients with cluster headache to those of healthy controls. We enrolled 23 patients with newly diagnosed cluster headache and 31 healthy controls. They underwent three-dimensional T1-weighted imaging utilizing a 3.0 Tesla MRI scanner. Volumetric analysis of the subcortical limbic structures, including the hippocampus, amygdala, thalamus, mammillary body, hypothalamus, basal forebrain, septal nuclei, fornix, and nucleus accumbens, was performed. We examined the limbic covariance network using a graph theory. The volumes of the limbic structures between patients with cluster headache and healthy controls were significantly different. The volume of the left hippocampus in patients with cluster headache was significantly lower than that in healthy controls (0.256 vs 0.291 %, p = 0.002). Patients with cluster headache showed significant alterations of the limbic covariance network. The average strength, global efficiency, local efficiency, mean clustering coefficient, and transitivity were lower (5.238 vs 10.322, p = 0.030; 0.355 vs 0.608, p = 0.020; 0.547 vs 1.553, p = 0.020; 0.424 vs 0.895, p = 0.016; respectively), whereas the characteristic path length was higher (3.314 vs 1.752, p = 0.040) in patients with cluster headache than in healthy controls. We detected alterations of limbic structure volumes in patients with cluster headache compared to healthy controls, especially in the hippocampus. We also found significant alterations in the limbic covariance network in patients with cluster headache who showed decreased segregation and integration. These abnormalities could be related to the pathophysiology of cluster headache.

Limbic covariance network alterations in patients with transient global amnesia

BACKGROUND

We compared limbic structure volumes and graph theory parameters of the limbic covariance network between patients with transient global amnesia (TGA) and healthy controls, and between patients with single and recurrent TGA events.

METHODS

We retrospectively enrolled 122 patients with TGA (single event, n = 107; recurrent events, n = 15) and 50 healthy controls who underwent three-dimensional T1-weighted MRI imaging of the brain. Volumetric analysis of the subcortical limbic structures, including the hippocampus, amygdala, thalamus, mammillary body, hypothalamus, basal forebrain, septal nuclei, fornix, and nucleus accumbens, was performed. We examined the limbic covariance network using a graph theory.

RESULTS

Limbic structure volumes did not differ between patients with TGA and healthy controls, and between patients with a single event and those with recurrent events. However, the radius of the limbic covariance network was significantly greater in patients with TGA than in healthy controls (6.595 vs. 4.564, p = 0.040). Furthermore, the radius, diameter, eccentricity, and characteristics path length were greater (4.066 vs. 2.000, p = 0.009; 7.062 vs. 3.645, p = 0.029; 5.633 vs. 2.774, p = 0.013; 3.373 vs. 1.688, p = 0.004; respectively), whereas the average strength, global efficiency, local efficiency, mean clustering coefficient, transitivity, and small-worldness index were lower (5.595 vs. 10.831, p = 0.004; 0.350 vs. 0.642, p = 0.002; 0.531 vs. 1.724, p = 0.004; 0.304 vs. 0.624, p = 0.006; 0.456 vs. 0.935, p = 0.003; 0.913 vs. 0.993, p = 0.017; respectively), in patients with recurrent events than in those with a single event.

CONCLUSION

The limbic covariance network shows significant alterations in patients with TGA, as well as differences between patients with recurrent events and those with a single event. These findings suggest that changes in the limbic covariance network could be related to the pathogenesis of TGA.

Deep Learning-Based Diagnosis of Alzheimer’s Disease

Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.