High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas

Deviations in Hippocampal Subregion in Older Adults With Cognitive Frailty

BACKGROUND

Cognitive frailty is a particular state of cognitive vulnerability toward dementia with neuropathological hallmarks. The hippocampus is a complex, heterogeneous structure closely relates to the cognitive impairment in elderly which is composed of 12 subregions. Atrophy of these subregions has been implicated in a variety of neurodegenerative diseases. The aim of this study was to explore the changes in hippocampal subregions in older adults with cognitive frailty and the relationship between subregions and cognitive impairment as well as physical frailty.

METHODS

Twenty-six older adults with cognitive frailty and 26 matched healthy controls were included in this study. Cognitive function was evaluated by the Montreal Cognitive Assessment (MoCA) scale (Fuzhou version) and Wechsler Memory Scale-Revised Chinese version (WMS-RC), while physical frailty was tested with the Chinese version of the Edmonton Frailty Scale (EFS) and grip strength. The volume of the hippocampal subregions was measured with structural brain magnetic resonance imaging. Partial correlation analysis was carried out between the volumes of hippocampal subregions and MoCA scores, Wechsler's Memory Quotient and physical frailty indexes.

RESULTS

A significant volume decrease was found in six hippocampal subregions, including the bilateral presubiculum, the left parasubiculum, molecular layer of the hippocampus proper (molecular layer of the HP), and hippocampal amygdala transition area (HATA), and the right cornu ammonis subfield 1 (CA1) area, in older adults with cognitive frailty, while the proportion of brain parenchyma and total number of white matter fibers were lower than those in the healthy controls. Positive correlations were found between Wechsler's Memory Quotient and the size of the left molecular layer of the HP and HATA and the right presubiculum. The sizes of the left presubiculum, molecular of the layer HP, and HATA and right CA1 and presubiculum were found to be positively correlated with MoCA score. The sizes of the left parasubiculum, molecular layer of the HP and HATA were found to be negatively correlated with the physical frailty index.

CONCLUSION

Significant volume decrease occurs in hippocampal subregions of older adults with cognitive frailty, and these changes are correlated with cognitive impairment and physical frailty. Therefore, the atrophy of hippocampal subregions could participate in the pathological progression of cognitive frailty.

A Review of Publicly Available Automatic Brain Segmentation Methodologies, Machine Learning Models, Recent Advancements, and Their Comparison

BACKGROUND

The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer's disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the "gold standard" is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods.

PURPOSE

The purpose of the study is to understand the current and emerging state of automatic segmentation tools, their comparison, machine learning models, their reliability, and shortcomings with an intent to focus on the development of improved methods and algorithms.

METHODS

The study focuses on the review of publicly available neuroimaging tools, their comparison, and emerging machine learning models particularly those based on CNN architecture developed and published during the last five years.

CONCLUSION

Several software tools developed by various research groups and made publicly available for automatic segmentation of the brain show variability in their results in several comparison studies and have not attained the level of reliability required for clinical studies. The machine learning models particularly three dimensional fully convolutional network models can provide a robust and efficient alternative with relation to publicly available tools but perform poorly on unseen datasets. The challenges related to training, computation cost, reproducibility, and validation across distinct scanning modalities for machine learning models need to be addressed.

Recent cannabis use is associated with smaller hippocampus volume: High-resolution segmentation of structural subfields in a large non-clinical sample

There is mixed evidence that individuals who use cannabis have reduced hippocampal and amygdalar gray matter volume, potentially because of small sample sizes and imprecise morphological characterization. New automated segmentation procedures have improved the measurement of these structures and allow better examination of their subfields, which have been linked to distinct aspects of memory and emotion. The current study applies this new segmentation procedure to the Human Connectome Project Young Adult dataset (N = 1080) to investigate associations of cannabis use with gray matter volume in the hippocampus and amygdala. Results revealed significant bilateral inverse associations of hippocampal volume with recent cannabis use (THC+ urine drug screen; P < .005). Hippocampal subfield analyses indicated these associations were primarily driven by the head of the hippocampus, the first section of the cornu amonis (CA1), the subicular complex, and the molecular layer of the hippocampus. No associations were detected for age of cannabis initiation, the frequency of cannabis use across the lifespan, or the lifetime presence of cannabis use disorder. In one of the largest studies to date, these results support the hypothesis that recent cannabis use is linked to reduced hippocampal volume, but that this effect may dissipate following prolonged abstinence. Furthermore, these results clarify the specific subfields which may be most associated with recent cannabis use.

Differential early subcortical involvement in genetic FTD within the GENFI cohort

BACKGROUND

Studies have previously shown evidence for presymptomatic cortical atrophy in genetic FTD. Whilst initial investigations have also identified early deep grey matter volume loss, little is known about the extent of subcortical involvement, particularly within subregions, and how this differs between genetic groups.

METHODS

480 mutation carriers from the Genetic FTD Initiative (GENFI) were included (198 GRN, 202 C9orf72, 80 MAPT), together with 298 non-carrier cognitively normal controls. Cortical and subcortical volumes of interest were generated using automated parcellation methods on volumetric 3 T T1-weighted MRI scans. Mutation carriers were divided into three disease stages based on their global CDR® plus NACC FTLD score: asymptomatic (0), possibly or mildly symptomatic (0.5) and fully symptomatic (1 or more).

RESULTS

In all three groups, subcortical involvement was seen at the CDR 0.5 stage prior to phenoconversion, whereas in the C9orf72 and MAPT mutation carriers there was also involvement at the CDR 0 stage. In the C9orf72 expansion carriers the earliest volume changes were in thalamic subnuclei (particularly pulvinar and lateral geniculate, 9-10%) cerebellum (lobules VIIa-Crus II and VIIIb, 2-3%), hippocampus (particularly presubiculum and CA1, 2-3%), amygdala (all subregions, 2-6%) and hypothalamus (superior tuberal region, 1%). In MAPT mutation carriers changes were seen at CDR 0 in the hippocampus (subiculum, presubiculum and tail, 3-4%) and amygdala (accessory basal and superficial nuclei, 2-4%). GRN mutation carriers showed subcortical differences at CDR 0.5 in the presubiculum of the hippocampus (8%).

CONCLUSIONS

C9orf72 expansion carriers show the earliest and most widespread changes including the thalamus, basal ganglia and medial temporal lobe. By investigating individual subregions, changes can also be seen at CDR 0 in MAPT mutation carriers within the limbic system. Our results suggest that subcortical brain volumes may be used as markers of neurodegeneration even prior to the onset of prodromal symptoms.

Evolution of Human Brain Atlases in Terms of Content, Applications, Functionality, and Availability

Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Content-wise, new electronic atlases are categorized into eight groups considering their scope, parcellation, modality, plurality, scale, ethnicity, abnormality, and a mixture of them. Atlas content developments in these groups are heading in 23 various directions. Application-wise, we overview atlases in neuroeducation, research, and clinics, including stereotactic and functional neurosurgery, neuroradiology, neurology, and stroke. Functionality-wise, tools and functionalities are addressed for atlas creation, navigation, individualization, enabling operations, and application-specific. Availability is discussed in media and platforms, ranging from mobile solutions to leading-edge supercomputers, with three accessibility levels. The major application-wise shift has been from research to clinical practice, particularly in stereotactic and functional neurosurgery, although clinical applications are still lagging behind the atlas content progress. Atlas functionality also has been relatively neglected until recently, as the management of brain data explosion requires powerful tools. We suggest that the future human brain atlas-related research and development activities shall be founded on and benefit from a standard framework containing the core virtual brain model cum the brain atlas platform general architecture.

FreeSurfer ‐based segmentation of hippocampal subfields: A review of methods and applications, with a novel quality control procedure for ENIGMA studies and other collaborative efforts

Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013–12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi‐)genetics. Finally, we highlight points where FreeSurfer‐based hippocampal subfield studies may be optimized.

Restructuring of amygdala subregion apportion across adolescence

Total amygdala volumes develop in association with sex and puberty, and postmortem studies find neuronal numbers increase in a nuclei specific fashion across development. Thus, amygdala subregions and composition may evolve with age. Our goal was to examine if amygdala subregion absolute volumes and/or relative proportion varies as a function of age, sex, or puberty in a large sample of typically developing adolescents (N = 408, 43 % female, 10-17 years). Utilizing the in vivo CIT168 atlas, we quantified 9 subregions and implemented Generalized Additive Mixed Models to capture potential non-linear associations with age and pubertal status between sexes. Only males showed significant age associations with the basolateral ventral and paralaminar subdivision (BLVPL), central nucleus (CEN), and amygdala transition area (ATA). Again, only males showed relative differences in the proportion of the BLVPL, CEN, ATA, along with lateral (LA) and amygdalostriatal transition area (ASTA), with age. Using a best-fit modeling approach, age, and not puberty, was found to drive these associations. The results suggest that amygdala subregions show unique variations with age in males across adolescence. Future research is warranted to determine if our findings may contribute to sex differences in mental health that emerge across adolescence.

The Influence of Aging, Hearing, and Tinnitus on the Morphology of Cortical Gray Matter, Amygdala, and Hippocampus

Age related hearing loss (presbycusis) is a natural process represented by elevated auditory thresholds and decreased speech intelligibility, especially in noisy conditions. Tinnitus is a phantom sound that also potentially leads to cortical changes, with its highest occurrence coinciding with the clinical onset of presbycusis. The aim of our project was to identify age, hearing loss and tinnitus related structural changes, within the auditory system and associated structures. Groups of subjects with presbycusis and tinnitus (22 subjects), with only presbycusis (24 subjects), young tinnitus patients with normal hearing (10 subjects) and young controls (17 subjects), underwent an audiological examination to characterize hearing loss and tinnitus. In addition, MRI (3T MR system, analysis in Freesurfer software) scans were used to identify changes in the cortical and subcortical structures. The following areas of the brain were analyzed: Heschl gyrus (HG), planum temporale (PT), primary visual cortex (V1), gyrus parahippocampus (PH), anterior insula (Ins), amygdala (Amg), and hippocampus (HP). A statistical analysis was performed in R framework using linear mixed-effects models with explanatory variables: age, tinnitus, laterality and hearing. In all of the cortical structures, the gray matter thickness decreased significantly with aging without having an effect on laterality (differences between the left and right hemispheres). The decrease in the gray matter thickness was faster in the HG, PT and Ins in comparison with the PH and V1. Aging did not influence the surface of the cortical areas, however there were differences between the surface size of the reported regions in the left and right hemispheres. Hearing loss caused only a borderline decrease of the cortical surface in the HG. Tinnitus was accompanied by a borderline decrease of the Ins surface and led to an increase in the volume of Amy and HP. In summary, aging is accompanied by a decrease in the cortical gray matter thickness; hearing loss only has a limited effect on the structure of the investigated cortical areas and tinnitus causes structural changes which are predominantly within the limbic system and insula, with the structure of the auditory system only being minimally affected.

Differential spatial patterns of structural connectivity of amygdala nuclei with orbitofrontal cortex

The orbitofrontal cortex (OFC)‐amygdala circuit is critical to goal‐directed behavior, learning, and valuation. However, our understanding of the OFC‐amygdala connections that support these emergent processes is hampered by our reliance on the primate literature and insufficient knowledge regarding the connectivity patterns between regions of OFC and amygdala nuclei, each of which is differentially involved in these processes in humans. Thus, we examined structural connectivity between different OFC regions and four amygdala nuclei in healthy adults (n = 1,053) using diffusion‐based anatomical networks and probabilistic tractography in four conceptually distinct ways. First, we identified the OFC regions that connect with each nucleus. Second, we identified the OFC regions that were more likely to connect with a given nucleus than the others. Finally, we developed probabilistic and rank‐order maps of OFC (one for each nucleus) based upon the likelihood of each OFC voxel exhibiting preferential connectivity with each nucleus and the relative density of connectivity between each OFC voxel and each nucleus, respectively. The first analyses revealed that the connections of each nucleus spanned all of OFC, reflecting widespread overall amygdala linkage with OFC. Analysis of preferential connectivity and probabilistic and rank‐order maps of OFC converged to reveal differential patterns of connectivity between OFC and each nucleus. Present findings illustrate the importance of accounting for spatial specificity when examining links between OFC and amygdala. This fine‐grained examination of OFC‐amygdala connectivity can be applied to understand how such connectivity patterns support a range of emergent functions including affective and motivational processes.

Contributions of Hippocampal Volume to Cognition in Healthy Older Adults

Objective: The association between hippocampal volume and memory is continuing to be characterized in healthy older adults. Prior research suggests smaller hippocampal volume in healthy older adults is associated with poorer episodic memory and processing speed, as well as working memory, verbal learning, and executive functioning as measured by the NIH Toolbox Fluid (Fluid Cognition Composite, FCC) and Crystalized Cognition Composites (CCC). This study aimed to replicate these findings and to evaluate the association between: (1) hippocampal asymmetry index and cognition; and (2) independent contributions of the left and right hippocampal volume and cognition in a large sample of healthy older adults. Participants and Methods: One-hundred and eighty-three healthy older adults (M age = 71.72, SD = 5.3) received a T1-weighted sequence on a 3T scanner. Hippocampal subfields were extracted using FreeSurfer 6.0 and combined to provide left, right, and total hippocampal volumes. FCC subtests include Dimensional Change Card Sort, Flanker Inhibitory Control and Attention, List Sorting, Picture Sequence Memory, and Pattern Comparison. CCC subtests include Picture Vocabulary and Oral Reading Recognition. Multiple linear regressions were performed predicting cognition composites from the total, left and right, and asymmetry of hippocampal volume, controlling for sex, education, scanner, and total intracranial volume. Multiple comparisons in primary analyses were corrected using a false discovery rate (FDR) of p < 0.05. Results: FCC scores were positively associated with total (β = 0.226, FDR q = 0.044) and left (β = 0.257, FDR q = 0.024) hippocampal volume. Within FCC, Picture Sequence Memory scores positively associated with total (β = 0.284, p = 0.001) and left (β = 0.98, p = 0.001) hippocampal volume. List Sorting scores were also positively associated with left hippocampal volume (β = 0.189, p = 0.029). Conclusions: These results confirm previous research suggesting that bilateral hippocampal volume is associated with FCC, namely episodic memory. The present study also suggests the left hippocampal volume may be more broadly associated with both episodic and working memory. Studies should continue to investigate lateralized hippocampal contributions to aging processes to better identify predictors of cognitive decline.

Multimodal hippocampal and amygdala subfield volumetry in polygenic risk for Alzheimer's disease

Preclinical models of Alzheimer's disease (AD) suggest that volumetric reductions in medial temporal lobe (MTL) structures manifest before clinical onset. AD polygenic risk scores (PRSs) are further linked to reduced MTL volumes (the hippocampus/amygdala); however, the relationship between the PRS and specific subregions remains unclear. We determine the relationship between the AD-PRSs and MTL subregions in a large sample of young participants (N = 730, aged 22–35 years) using a multimodal (T1w/T2w) approach. We first demonstrate that the PRSs for the hippocampus/amygdala predict their respective volumes and specific hippocampal subregions (pFDR < 0.05). We further observe negative relationships between the AD-PRSs and whole hippocampal/amygdala volumes. Critically, we demonstrate novel associations between the AD-PRSs and specific hippocampal subfields such as CA1 (β = −0.096, pFDR = 0.045) and the fissure (β = −0.101, pFDR = 0.041). We provide evidence that the AD-PRS is linked to specific MTL subfields decades before AD onset. This may help inform preclinical models of AD risk, providing additional specificity for intervention and further insight into mechanisms by which common AD variants confer susceptibility.

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Polygenic risk for Alzheimer's disease (AD-PRS) explains significant proportion of AD.

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AD-PRS also linked to hippocampus and amygdala volume.

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AD-PRS is negatively associated with specific hippocampal subfields.

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Polygenic AD models help us understand genetic contributions to medial temporal lobe nuclei.

The amygdala in adolescents with attention-deficit/hyperactivity disorder: Structural and functional correlates of delay aversion

OBJECTIVES

Recent magnetic resonance imaging (MRI) studies implicate structural alterations of amygdala, a brain region responsible for processing and experiencing negative emotions, in adolescents with attention-deficit/hyperactivity disorder (ADHD). Here we examined ADHD-related structural correlates of amygdala functional activity elicited during a functional MRI task designed to test behavioural and brain responses to the imposition of delay - an event known to both elicit amygdala hyperactivation and aversity in ADHD.

METHODS

Structural MRI scans from 28 right-handed male adolescents with combined type ADHD and 32 age-matched controls were analysed. Regional grey matter volumes of ADHD and control participants (P[FWE] < 0.05) were correlated with delay aversion self-ratings and neural activity in response to delay-related cues on the Escape Delay Incentive fMRI task.

RESULTS

ADHD was associated with significantly reduced volumes in bilateral amygdala, parahippocampal and temporal gyrus (P[FWE] < 0.05), greater basolateral amygdala activation to delay-related cues (P[FWE] < 0.05) and higher delay aversion self-ratings. Amygdala volume reductions were significantly correlated with, and statistically mediated the pathway from ADHD to, delay-cue-related amygdala hyperactivity (P < 0.01) and self-reported delay aversion (P < 0.01).

CONCLUSIONS

We provide the first evidence of the functional significance of reduced amygdala volumes in adolescents with ADHD by highlighting its relation to delay-induced brain activity that is linked to delay aversion.

Subcortical surface morphometry in substance dependence: An ENIGMA addiction working group study

While imaging studies have demonstrated volumetric differences in subcortical structures associated with dependence on various abused substances, findings to date have not been wholly consistent. Moreover, most studies have not compared brain morphology across those dependent on different substances of abuse to identify substance-specific and substance-general dependence effects. By pooling large multinational datasets from 33 imaging sites, this study examined subcortical surface morphology in 1628 nondependent controls and 2277 individuals with dependence on alcohol, nicotine, cocaine, methamphetamine, and/or cannabis. Subcortical structures were defined by FreeSurfer segmentation and converted to a mesh surface to extract two vertex-level metrics-the radial distance (RD) of the structure surface from a medial curve and the log of the Jacobian determinant (JD)-that, respectively, describe local thickness and surface area dilation/contraction. Mega-analyses were performed on measures of RD and JD to test for the main effect of substance dependence, controlling for age, sex, intracranial volume, and imaging site. Widespread differences between dependent users and nondependent controls were found across subcortical structures, driven primarily by users dependent on alcohol. Alcohol dependence was associated with localized lower RD and JD across most structures, with the strongest effects in the hippocampus, thalamus, putamen, and amygdala. Meanwhile, nicotine use was associated with greater RD and JD relative to nonsmokers in multiple regions, with the strongest effects in the bilateral hippocampus and right nucleus accumbens. By demonstrating subcortical morphological differences unique to alcohol and nicotine use, rather than dependence across all substances, results suggest substance-specific relationships with subcortical brain structures.

Adults vs. neonates: Differentiation of functional connectivity between the basolateral amygdala and occipitotemporal cortex

The amygdala, a subcortical structure known for social and emotional processing, consists of multiple subnuclei with unique functions and connectivity patterns. Tracer studies in adult macaques have shown that the basolateral subnuclei differentially connect to parts of visual cortex, with stronger connections to anterior regions and weaker connections to posterior regions; infant macaques show robust connectivity even with posterior visual regions. Do these developmental differences also exist in the human amygdala, and are there specific functional regions that undergo the most pronounced developmental changes in their connections with the amygdala? To address these questions, we explored the functional connectivity (from resting-state fMRI data) of the basolateral amygdala to occipitotemporal cortex in human neonates scanned within one week of life and compared the connectivity patterns to those observed in young adults. Specifically, we calculated amygdala connectivity to anterior-posterior gradients of the anatomically-defined occipitotemporal cortex, and also to putative occipitotemporal functional parcels, including primary and high-level visual and auditory cortices (V1, A1, face, scene, object, body, high-level auditory regions). Results showed a decreasing gradient of functional connectivity to the occipitotemporal cortex in adults-similar to the gradient seen in macaque tracer studies-but no such gradient was observed in neonates. Further, adults had stronger connections to high-level functional regions associated with face, body, and object processing, and weaker connections to primary sensory regions (i.e., A1, V1), whereas neonates showed the same amount of connectivity to primary and high-level sensory regions. Overall, these results show that functional connectivity between the amygdala and occipitotemporal cortex is not yet differentiated in neonates, suggesting a role of maturation and experience in shaping these connections later in life.

Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling

Transcranial brain stimulation (TBS) has been established as a method for modulating and mapping the function of the human brain, and as a potential treatment tool in several brain disorders. Typically, the stimulation is applied using a one-size-fits-all approach with predetermined locations for the electrodes, in electric stimulation (TES), or the coil, in magnetic stimulation (TMS), which disregards anatomical variability between individuals. However, the induced electric field distribution in the head largely depends on anatomical features implying the need for individually tailored stimulation protocols for focal dosing. This requires detailed models of the individual head anatomy, combined with electric field simulations, to find an optimal stimulation protocol for a given cortical target. Considering the anatomical and functional complexity of different brain disorders and pathologies, it is crucial to account for the anatomical variability in order to translate TBS from a research tool into a viable option for treatment. In this article we present a new method, called CHARM, for automated segmentation of fifteen different head tissues from magnetic resonance (MR) scans. The new method compares favorably to two freely available software tools on a five-tissue segmentation task, while obtaining reasonable segmentation accuracy over all fifteen tissues. The method automatically adapts to variability in the input scans and can thus be directly applied to clinical or research scans acquired with different scanners, sequences or settings. We show that an increase in automated segmentation accuracy results in a lower relative error in electric field simulations when compared to anatomical head models constructed from reference segmentations. However, also the improved segmentations and, by implication, the electric field simulations are affected by systematic artifacts in the input MR scans. As long as the artifacts are unaccounted for, this can lead to local simulation differences up to 30% of the peak field strength on reference simulations. Finally, we exemplarily demonstrate the effect of including all fifteen tissue classes in the field simulations against the standard approach of using only five tissue classes and show that for specific stimulation configurations the local differences can reach 10% of the peak field strength.

Experimental data on demographic, functional and structures of patients with schizophrenia and schizophrenia-dementia

Schizophrenia is a severe mental disorder that includes behavioural and cognitive manifestations generated by genetic or environmental factors, caused by a dysfunction of the dopaminergic system which contributes to the genesis of psychosis, producing a profound effect on affected individuals and society [6]. In this work, demographic data, neuropsychological profiles and measurements of brain morphometry, using Magnetic Resonance Image (MRI), of three groups of patients are presented. A control group with 15 patients, a schizophrenic without dementia group with 10 patients, and a schizophrenic with dementia group with 10 patients constituted the observed sample. Results of 21 neuropsychological tests and 11 brain structure measurements are included. The data set is a comprehensive source for advancing in a further understanding of schizophrenia and schizophrenia-dementia neuro-pathologies.

Do neurobiological differences exist between paranoid and non-paranoid schizophrenia? Findings from the bipolar schizophrenia network on intermediate phenotypes study

Subtypes of schizophrenia, constructed using clinical phenomenology to resolve illness heterogeneity, have faced criticism due to overlapping symptomatology and longitudinal instability; they were therefore dropped from the Diagnostic Statistical Manual-5. Cognitive and imaging findings comparing paranoid (P-SZ) and non-paranoid (disorganized, residual and undifferentiated; NP-SZ) schizophrenia have been limited due to small sample sizes. We assessed P-SZ and NP-SZ using symptomatology, cognition and brain structure and predicted that there would be few neurobiological differences. P-SZ (n = 237), NP-SZ (n = 127) and controls (n = 430) were included from a multi-site study. In a subset of this sample, structural imaging measures (P-SZ, n = 133; NP-SZ, n = 67; controls, n = 310) were calculated using Freesurfer 6.0. Group contrasts were run using analysis of covariance, controlling for age, sex, race and site, p-values were corrected using False Discovery Rate (FDR) and were repeated excluding the residual subtype. Compared to NP-SZ (with and without the residual subtype), P-SZ displayed fewer negative symptoms, faster speed of processing, larger bilateral hippocampus, right amygdala and their subfield volumes. Additionally, NP-SZ (with residual subtype) displayed fewer depressive symptoms and higher left transverse temporal cortical thickness (CT) but NP-SZ without residual subtype showed lower GAF scores and worse digit sequencing compared to P-SZ. No differences in positive symptoms and functioning (global or social) were detected. Subtle but significant differences were seen in cognition, symptoms, CT and subcortical volumes between P-SZ and NP-SZ. While the magnitude of these differences is not large enough to justify them as distinct categories, the paranoid- nonparanoid distinction in schizophrenia merits further investigation.

Anatomic alterations across amygdala subnuclei in medication-free patients with obsessive-compulsive disorder

BACKGROUND

The amygdala has been implicated in obsessive-compulsive disorder (OCD), a common, disabling illness. However, the regional distribution of anatomic alterations in this structure and their association with the symptoms of OCD remains to be established.

METHODS

We collected high-resolution 3D T1-weighted images from 81 untreated patients with OCD and no lifetime history of comorbid psychotic, affective or anxiety disorders, and from 95 age- and sex-matched healthy controls. We extracted the volume of the central nucleus of the amygdala (CeA) and the basolateral complex of the amygdala (BLA) and compared them across groups using FreeSurfer 6.0. In exploratory analyses, we evaluated other subnuclei, including the cortical medial nuclei, the anterior amygdaloid area, and the corticoamygdaloid transition area.

RESULTS

Patients with OCD had reduced amygdala volume bilaterally compared with healthy controls (left, p = 0.034; right, p = 0.002). Volume reductions were greater in the CeA (left: -11.9%, p = 0.002; right: -13.3%, p < 0.001) than in the BLA (left lateral nucleus: -3.3%, p = 0.029; right lateral nucleus: -3.9%, p = 0.018; right basal nucleus: -4.1%, p = 0.017; left accessory basal nucleus: -6.5%, p = 0.001; right accessory basal nucleus: -9.3%, p < 0.001). Volume reductions in the CeA were associated with illness duration. Exploratory analysis revealed smaller medial (left: -15.4%, p < 0.001, η2 = 0.101) and cortical (left: -9.1%, p = 0.001, η2 = 0.058; right: -15.4%, p < 0.001, η2 = 0.175) nuclei in patients with OCD compared with healthy controls.

LIMITATIONS

Although the strict exclusion criteria used in the study helped us to identify OCD-specific alterations, they may have limited generalizability to the broader OCD population.

CONCLUSION

Our results provide a comprehensive anatomic profile of alterations in the amygdala subnuclei in untreated patients with OCD and highlight a distinctive pattern of volume reductions across subnuclei in OCD. Based on the functional properties of the amygdala subnuclei established from preclinical research, CeA impairment may contribute to behavioural inflexibility, and BLA disruption may be responsible for altered fear conditioning and the affective components of OCD.

Developmental Variation in Amygdala Volumes: Modeling Differences Across Time, Age, and Puberty

BACKGROUND

There is evidence that the amygdala undergoes extensive development. The exact nature of this change remains less clear, with evidence suggesting linear, curvilinear, and null effects. The aim of this study was the identification of a normative reference of left and right amygdala development by parceling variance into separate effects of age and longitudinal growth.

METHODS

Data came from the National Institutes of Health MRI Study of Normal Brain Development. Participants in this sample were 54% female and ranged in age from 5 to 18 years (mean = 11.37 years) at study entry.

RESULTS

As predicted, the age at initial scan moderated the slope of both left and right amygdala volumes, demonstrating that the nature of longitudinal growth varies across age (i.e., steeper slopes observed among those first scanned at an early age). Follow-up analysis showed that the positive longitudinal growth slope becomes nonsignificant at 13.1 years of age for the left amygdala and at 14.5 years for the right amygdala, suggesting that growth of the left amygdala peaks earlier than growth of the right amygdala.

CONCLUSIONS

Findings suggest that rapid increases in volumes at early ages decline as youths enter adolescence and may turn to minor declines in volume during late adolescence or early adulthood.

The association of PTSD symptom severity with amygdala nuclei volumes in traumatized youths

The amygdala is a core component in neurobiological models of stress and stress-related pathologies, including post-traumatic stress disorder (PTSD). While numerous studies have reported increased amygdala activity following traumatic stress exposure and in PTSD, the findings regarding amygdala volume have been mixed. One reason for these mixed findings may be that the amygdala has been considered as a homogenous entity, while it in fact consists of several nuclei with unique cellular and connectivity profiles. Here, we investigated amygdala nuclei volumes of the basolateral and the centrocorticomedial complex in relation to PTSD symptom severity in 47 young survivors from the 2011 Norwegian terror attack 24-36 months post-trauma. PTSD symptoms were assessed 4-5, 14-15 and 24-36 months following the trauma. We found that increased PTSD symptom severity 24-36 months post-trauma was associated with volumetric reductions of all basolateral as well as the central and the medial nuclei. However, only the lateral nucleus was associated with longitudinal symptom development, and mediated the association between 4-5 months and 24-36 months post-trauma symptoms. The results suggest that the amygdala nuclei may be differentially associated with cross-sectional and longitudinal measures of PTSD symptom severity. As such, investigations of amygdala total volume may not provide an adequate index of the association between amygdala and stress-related mental illness.

Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions

Structural changes in the hippocampus and amygdala have been demonstrated in schizophrenia patients. However, whether morphological information from these subcortical regions could be used by machine learning algorithms for schizophrenia classification were unknown. The aim of this study was to use volume of the amygdaloid and hippocampal subregions for schizophrenia classification. The dataset consisted of 57 patients with schizophrenia and 69 healthy controls. The volume of 26 hippocampal and 20 amygdaloid subregions were extracted from T1 structural MRI images. Sequential backward elimination (SBE) algorithm was used for feature selection, and a linear support vector machine (SVM) classifier was configured to explore the feasibility of hippocampal and amygdaloid subregions in the classification of schizophrenia. The proposed SBE-SVM model achieved a classification accuracy of 81.75% on 57 patients and 69 healthy controls, with a sensitivity of 84.21% and a specificity of 81.16%. AUC was 0.8241 (p < 0.001 tested with 1000-times permutation). The results demonstrated evidence of hippocampal and amygdaloid structural changes in schizophrenia patients, and also suggested that morphological features from the amygdaloid and hippocampal subregions could be used by machine learning algorithms for the classification of schizophrenia.

An emotional-response model of bipolar disorders integrating recent findings on amygdala circuits

Because of our classification system limitations for defining psychiatric disorders and understanding their physiopathology, a new research area based on dimensions has emerged. It consists of exploring domains derived from fundamental behavioral components linked to neurobiological systems. Emotional processing is among the most affected dimensions in bipolar disorders (BD), but is excluded from the definition criteria. The purpose of this review is to synthesize the emotional responses disruption during the different phases of BD, using intensity and valence as the two key characteristics of emotions. We integrate those emotional disruptions into an original, emotion-based model contrasting with the current diagnostic frame built on mood. Emotional processing is underpinned by cortico-limbic circuits involving the amygdala. Recent publications showed the crucial role of the amygdala in emotional processes triggered by stimuli of negative, but also positive valence. We show how these neuroscience data can provide physiological basis for emotional disturbances observed in BD. We conclude with translational perspectives to improve the current knowledge about neural substrates underlying altered emotional responses characterizing BD.

Amygdala pathology in amyotrophic lateral sclerosis and primary lateral sclerosis

Temporal lobe studies in motor neuron disease overwhelmingly focus on white matter alterations and cortical grey matter atrophy. Reports on amygdala involvement are conflicting and the amygdala is typically evaluated as single structure despite consisting of several functionally and cytologically distinct nuclei. A prospective, single-centre, neuroimaging study was undertaken to comprehensively characterise amygdala pathology in 100 genetically-stratified ALS patients, 33 patients with PLS and 117 healthy controls. The amygdala was segmented into groups of nuclei using a Bayesian parcellation algorithm based on a probabilistic atlas and shape deformations were additionally assessed by vertex analyses. The accessory basal nucleus (p = .021) and the cortical nucleus (p = .022) showed significant volume reductions in C9orf72 negative ALS patients compared to controls. The lateral nucleus (p = .043) and the cortico-amygdaloid transition (p = .024) were preferentially affected in C9orf72 hexanucleotide carriers. A trend of total volume reduction was identified in C9orf72 positive ALS patients (p = .055) which was also captured in inferior-medial shape deformations on vertex analyses. Our findings highlight that the amygdala is affected in ALS and our study demonstrates the selective involvement of specific nuclei as opposed to global atrophy. The genotype-specific patterns of amygdala involvement identified by this study are consistent with the growing literature of extra-motor clinical features. Mesial temporal lobe pathology in ALS is not limited to hippocampal pathology but, as a key hub of the limbic system, the amygdala is also affected in ALS.

Amygdala Structural Connectivity Is Associated With Impulsive Choice and Difficulty Quitting Smoking

Introduction: The amygdala is known to play a role in mediating emotion and possibly addiction. We used probabilistic tractography (PT) to evaluate whether structural connectivity of the amygdala to the brain reward network is associated with impulsive choice and tobacco smoking.

Methods: Diffusion and structural MRI scans were obtained from 197 healthy subjects (45 with a history of tobacco smoking) randomly sampled from the Human Connectome database. PT was performed to assess amygdala connectivity with several brain regions. Seed masks were generated, and statistical maps of amygdala connectivity were derived. Connectivity results were correlated with a subject performance both on a delayed discounting task and whether they met specified criteria for difficulty quitting smoking.

Results: Amygdala connectivity was spatially segregated, with the strongest connectivity to the hippocampus, orbitofrontal cortex (OFC), and brainstem. Connectivity with the hippocampus was associated with preference for larger delayed rewards, whereas connectivity with the OFC, rostral anterior cingulate cortex (rACC), and insula were associated with preference for smaller immediate rewards. Greater nicotine dependence with difficulty quitting was associated with less hippocampal and greater brainstem connectivity. Scores on the Fagerstrom Test for Nicotine Dependence (FTND) correlated with rACC connectivity.

Discussion: These findings highlight the importance of the amygdala-hippocampal-ACC network in the valuation of future rewards and substance dependence. These results will help to identify potential targets for neuromodulatory therapies for addiction and related disorders.

Identifying subtypes of mild cognitive impairment from healthy aging based on multiple cortical features combined with volumetric measurements of the hippocampal subfields

Background

Mild cognitive impairment (MCI) is subtle cognitive decline with an estimated 10-15% yearly conversion rate toward Alzheimer's disease (AD). It remains unexplored in brain cortical association areas in different lobes and its changes with progression and conversion of MCI.

Methods

Brain structural magnetic resonance (MR) images were collected from 102 stable MCI (sMCI) patients. One hundred eleven were converted MCI (cMCI) patients, and 109 were normal control (NC). The cortical surface features and volumes of subcortical hippocampal subfields were calculated using the FreeSurfer software, followed by an analysis of variance (ANOVA) model, to reveal the differences between the NC-sMCI, NC-cMCI, and sMCI-cMCI groups. Afterward, the support vector machine-recursive feature elimination (SVM-RFE) method was applied to determine the differences between the groups.

Results

The experimental results showed that there were progressive degradations in either range or degree of the brain structure from NC to sMCI, and then to cMCI. The SVM classifier obtained accuracies with 64.62%, 78.96%, and 70.33% in the sMCI-NC, cMCI-NC, and cMCI-sMCI groups, respectively, using the volumes of hippocampal subfields independently. The combination of the volumes from the hippocampal subfields and cortical measurements could significantly increase the performance to 71.86%, 84.64%, and 76.86% for the sMCI-NC, cMCI-NC, and cMCI-sMCI classifications, respectively. Also, the brain regions corresponding to the dominant features with strong discriminative power were widely located in the temporal, frontal, parietal, olfactory cortexes, and most of the hippocampal subfields, which were associated with cognitive decline, memory impairment, spatial navigation, and attention control.

Conclusions

The combination of cortical features with the volumes of hippocampal subfields could supply critical information for MCI detection and its conversion.

Amygdala Nuclei Volumes Are Selectively Associated With Social Network Size in Homeless and Precariously Housed Persons

Objective: The amygdala is a brain region comprised of a group of functionally distinct nuclei that play a central role in social behavior. In homeless and precariously housed individuals, high rates of multimorbidity, and structural aspects of the environment may dysregulate social functioning. This study examined the neurobiological substrates of social connection in homeless and precariously housed persons by examining associations between amygdala nuclei volumes and social network size. Methods: Sixty participants (mean age 43.6 years; 73.3% male) were enrolled from an ongoing study of homeless and precariously housed adults in Vancouver, Canada. Social network size was assessed using the Arizona Social Support Interview Schedule. Amygdala nuclei volumes were extracted from anatomic T1-weighted MRI data. The central and basolateral amygdala nuclei were selected as they are implicated in anxiety-related and social behaviors. The hippocampus was included as a control brain region. Multivariable regression analysis investigated the relationship between amygdala nuclei volumes and social network size. Results: After controlling for age, sex, and total brain volume, individuals with the larger amygdala and central nucleus volumes had a larger network size. This association was not observed for the basolateral amygdala complex, though subsequent analysis found the basal and accessory basal nuclei of the basolateral amygdala were significantly associated with social network size. No association was found for the lateral amygdala nucleus or hippocampus. Conclusions: These findings suggest that select amygdala nuclei may be differentially involved in the social connections of persons with multimorbid illness and social marginalization.

A 3D Fully Convolutional Neural Network With Top-Down Attention-Guided Refinement for Accurate and Robust Automatic Segmentation of Amygdala and Its Subnuclei

Recent advances in deep learning have improved the segmentation accuracy of subcortical brain structures, which would be useful in neuroimaging studies of many neurological disorders. However, most existing deep learning based approaches in neuroimaging do not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the subnuclei of the amygdala. To tackle this challenging task, we developed a dual-branch dilated residual 3D fully convolutional network with parallel convolutions to extract more global context and alleviate the class imbalance issue by maintaining a small receptive field that is just the size of the regions of interest (ROIs). We also conduct multi-scale feature fusion in both parallel and series to compensate the potential information loss during convolutions, which has been shown to be important for small objects. The serial feature fusion enabled by residual connections is further enhanced by a proposed top-down attention-guided refinement unit, where the high-resolution low-level spatial details are selectively integrated to complement the high-level but coarse semantic information, enriching the final feature representations. As a result, the segmentations resulting from our method are more accurate both volumetrically and morphologically, compared with other deep learning based approaches. To the best of our knowledge, this work is the first deep learning-based approach that targets the subregions of the amygdala. We also demonstrated the feasibility of using a cycle-consistent generative adversarial network (CycleGAN) to harmonize multi-site MRI data, and show that our method generalizes well to challenging traumatic brain injury (TBI) datasets collected from multiple centers. This appears to be a promising strategy for image segmentation for multiple site studies and increased morphological variability from significant brain pathology.

Amygdalar nuclei and hippocampal subfields on MRI: Test-retest reliability of automated volumetry across different MRI sites and vendors

BACKGROUND

The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults.

METHODS

Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1-90 days). Segmentation was performed using FreeSurfer v6.0. Reliability was assessed using volume reproducibility error (ε) and spatial overlapping coefficient (DICE) between test and retest session.

RESULTS

Significant MRI site and vendor effects (p ​< ​.05) were found in a few subfields/nuclei for the ε, while extensive effects were found for the DICE score of most subfields/nuclei. Reliability was strongly influenced by volume, as ε correlated negatively and DICE correlated positively with volume size of structures (absolute value of Spearman's r correlations >0.43, p ​< ​1.39E-36). In particular, volumes larger than 200 ​mm³ (for amygdalar nuclei) and 300 ​mm³ (for hippocampal subfields, except for molecular layer) had the best test-retest reproducibility (ε ​< ​5% and DICE ​> ​0.80).

CONCLUSION

Our results support the use of volumetric measures of larger amygdalar nuclei and hippocampal subfields in multisite MRI studies. These measures could be useful for disease tracking and assessment of efficacy in drug trials.

Aberrant limbic brain structures in young individuals at risk for mental illness

AIM

Alterations in limbic structures may be present before the onset of serious mental illness, but whether subfield-specific limbic brain changes parallel stages in clinical risk is unknown. To address this gap, we compared the hippocampus, amygdala, and thalamus subfield-specific volumes in adolescents at various stages of risk for mental illness.

METHODS

MRI scans were obtained from 182 participants (aged 12-25 years) from the Canadian Psychiatric Risk and Outcome study. The sample comprised of four groups: asymptomatic youth at risk due to family history of mental illness (Stage 0, n = 32); youth with early symptoms of distress (Stage 1a, n = 41); youth with subthreshold psychotic symptoms (Stage 1b, n = 72); and healthy comparison participants with no family history of serious mental illness (n = 37). Analyses included between-group comparisons of brain measurements and correlational analyses that aimed to identify significant associations between neuroimaging and clinical measurements. A machine-learning technique examined the discriminative properties of the clinical staging model.

RESULTS

Subfield-specific limbic volume deficits were detected at every stage of risk for mental illness. A machine-learning classifier identified volume deficits within the body of the hippocampus, left amygdala nuclei, and medial-lateral nuclei of the thalamus that were most informative in differentiating between risk stages.

CONCLUSION

Aberrant subfield-specific changes within the limbic system may serve as biological evidence to support transdiagnostic clinical staging in mental illness. Differential patterns of volume deficits characterize those at risk for mental illness and may be indicative of a risk-stage progression.

Accuracy of automated amygdala MRI segmentation approaches in Huntington's disease in the IMAGE-HD cohort

Smaller manually-segmented amygdala volumes have been associated with poorer motor and cognitive function in Huntington's disease (HD). Manual segmentation is the gold standard in terms of accuracy; however, automated methods may be necessary in large samples. Automated segmentation accuracy has not been determined for the amygdala in HD. We aimed to determine which of three automated approaches would most accurately segment amygdalae in HD: FreeSurfer, FIRST, and ANTS nonlinear registration followed by FIRST segmentation. T1-weighted images for the IMAGE-HD cohort including 35 presymptomatic HD (pre-HD), 36 symptomatic HD (symp-HD), and 34 healthy controls were segmented using FreeSurfer and FIRST. For the third approach, images were nonlinearly registered to an MNI template using ANTS, then segmented using FIRST. All automated methods overestimated amygdala volumes compared with manual segmentation. Dice overlap scores, indicating segmentation accuracy, were not significantly different between automated approaches. Manually segmented volumes were most statistically differentiable between groups, followed by those segmented by FreeSurfer, then ANTS/FIRST. FIRST-segmented volumes did not differ between groups. All automated methods produced a bias where volume overestimation was more severe for smaller amygdalae. This bias was subtle for FreeSurfer, but marked for FIRST, and moderate for ANTS/FIRST. Further, FreeSurfer introduced a hemispheric bias not evident with manual segmentation, producing larger right amygdalae by 8%. To assist choice of segmentation approach, we provide sample size estimation graphs based on sample size and other factors. If automated segmentation is employed in samples of the current size, FreeSurfer may effectively distinguish amygdala volume between controls and HD.

In vivo staging of frontotemporal lobar degeneration TDP-43 type C pathology

BACKGROUND

TDP-43 type C is one of the pathological forms of frontotemporal lobar degeneration (FTLD) and mainly associated clinically with the semantic variant of primary progressive aphasia (svPPA). We aimed to define in vivo the sequential pattern of neuroanatomical involvement in a cohort of patients with FTLD-TDP type C pathology.

METHODS

We extracted the volumes of a set of cortical and subcortical regions from MRI scans of 19 patients with post mortem confirmed TDP-43 type C pathology (all with left hemisphere-predominant atrophy at baseline). In the initial development phase, we used w-scores computed from 81 cognitively normal controls to define a set of sequential stages of neuroanatomical involvement within the FTLD-TDP type C cohort where a w-score of < - 1.65 was considered abnormal. In a subsequent validation phase, we used 31 follow-up scans from 14 of the 19 patients in the same cohort to confirm the staging model.

RESULTS

Four sequential stages were identified in the initial development phase. Stage 1 was defined by atrophy in the left amygdala, medial temporal cortex, temporal pole, lateral temporal cortex and right medial temporal cortex; Stage 2 by atrophy in the left supratemporal cortex; Stage 3 by atrophy in the right anterior insula; and Stage 4 by atrophy in the right accumbens. In the validation phase, calculation of w-scores in the longitudinal scans confirmed the staging system, with all patients either staying in the same stage or progressing to a later stage at follow-up.

CONCLUSION

In vivo imaging is able to detect distinct stages of neuroanatomical involvement in FTLD-TDP type C pathology. Using an imaging-derived staging system allows a more refined stratification of patients with svPPA during life.

Specific and segregated changes to the functional connectome evoked by the processing of emotional faces: A task-based connectome study

The functional connectome is organized into several separable intrinsic connectivity networks (ICNs) that are thought to be the building blocks of the mind. However, it is currently not well understood how these networks are engaged by emotionally salient information, and how such engagement fits into emotion theories. The current study assessed how ICNs respond during the processing of angry and fearful faces in a large sample (N = 843) and examined how connectivity changes relate to the ICNs. All ICNs were modulated by emotional faces and showed functional interactions, a finding which is in line with the "theory of constructed emotions" that assumes that basic emotion do not arise from separable ICNs but from their interplay. We further identified a set of brain regions whose connectivity changes during the tasks suggest a special role as "affective hubs" in the brain. While hubs were located in all ICNs, we observed high selectivity for the amygdala within the subcortical network, a finding which also fits into "primary emotion" theory. The topology of hubs corresponded closely to a set of brain regions that has been implicated in anxiety disorders, pointing towards a clinical relevance of the present findings. The present data are the most comprehensive mapping of connectome-wide changes in functionally connectivity evoked by an affective processing task thus far and support two competing views on how emotions are represented in the brain, suggesting that the connectome paradigm might help with unifying the two ideas.

Mid age APOE ε4 carriers show memory-related functional differences and disrupted structure-function relationships in hippocampal regions

Carriers of the APOE e4 allele are at higher risk of age-related cognitive decline and Alzheimer’s disease (AD). The underlying neural mechanisms are uncertain, but genotype differences in medial temporal lobe (MTL) functional activity and structure at mid-age might contribute. We tested 16 non-e4 and 16 e4 carriers (aged 45–55) on a subsequent memory task in conjunction with MRI to assess how hippocampal volume (from T1 structural) and microstructure (neurite orientation-dispersion, from NODDI) differs by genotype and in relation to memory encoding. No previous study has investigated APOE effects on hippocampal microstructure using NODDI. Recall performance did not differ by genotype. A genotype by condition interaction in left parahippocampus indicated that in e4 carriers activity did not differentiate subsequently remembered from forgotten words. Hippocampal volumes and microstructure also did not differ by genotype but hippocampal volumes correlated positively with recognition performance in non-e4 carriers only. Similarly, greater hippocampal neurite orientation-dispersion was linked to better recall but only in non-e4s. Thus, we suggest that mid-age e4 carriers show a breakdown of normal MTL activation and structure-performance relationships. This could reflect an inability to utilise compensatory mechanisms, and contribute to higher risk of cognitive decline and AD in later life.

Individual-specific functional connectivity of the amygdala: A substrate for precision psychiatry

The amygdala is central to the pathophysiology of many psychiatric illnesses. An imprecise understanding of how the amygdala fits into the larger network organization of the human brain, however, limits our ability to create models of dysfunction in individual patients to guide personalized treatment. Therefore, we investigated the position of the amygdala and its functional subdivisions within the network organization of the brain in 10 highly sampled individuals (5 h of fMRI data per person). We characterized three functional subdivisions within the amygdala of each individual. We discovered that one subdivision is preferentially correlated with the default mode network; a second is preferentially correlated with the dorsal attention and fronto-parietal networks; and third subdivision does not have any networks to which it is preferentially correlated relative to the other two subdivisions. All three subdivisions are positively correlated with ventral attention and somatomotor networks and negatively correlated with salience and cingulo-opercular networks. These observations were replicated in an independent group dataset of 120 individuals. We also found substantial across-subject variation in the distribution and magnitude of amygdala functional connectivity with the cerebral cortex that related to individual differences in the stereotactic locations both of amygdala subdivisions and of cortical functional brain networks. Finally, using lag analyses, we found consistent temporal ordering of fMRI signals in the cortex relative to amygdala subdivisions. Altogether, this work provides a detailed framework of amygdala-cortical interactions that can be used as a foundation for models relating aberrations in amygdala connectivity to psychiatric symptoms in individual patients.

Amygdala subnucleus volumes in psychosis high-risk state and first-episode psychosis

Structural and functional abnormalities of the amygdala in schizophrenia have been well documented. Post-mortem studies suggest that the lateral nucleus is particularly affected in schizophrenia. It is not known whether the amygdala subnuclei are differently affected at the time of the first-episode psychosis or already at high-risk state. 75 first-episode psychosis patients (FEP), 45 clinical high-risk patients (CHR) and 76 population controls participated in this cross-sectional case-control study. Participants underwent T1-weighted 3T MRI scans, from which the amygdala was segmented using a newly developed automated algorithm. Because early adverse events increase risk for psychosis and affect the amygdala, we also tested whether experiences of childhood maltreatment associate with the putative amygdala subnuclei abnormalities. Compared to the population controls, FEP had smaller volumes of the lateral, and basal nuclei. In CHR, only the lateral nucleus was significantly smaller compared to the control subjects. Experience of childhood maltreatment was inversely associated with lateral nucleus volumes in FEP but not in CHR. These results show that the lateral and basal nuclei of the amygdala are already affected in FEP. These volumetric changes may reflect specific cellular abnormalities that have been observed in post-mortem studies in schizophrenia in the same subnuclei. Decreased volume of the lateral nucleus in CHR suggest that a smaller lateral nucleus could serve as a potential biomarker for psychosis risk. Finally, we found that the lateral nucleus volumes in FEP may be sensitive to the effects of childhood maltreatment.

Microstructural White Matter and Links With Subcortical Structures in Chronic Schizophrenia: A Free-Water Imaging Approach

Schizophrenia is a severe mental disorder with often a chronic course. Neuroimaging studies report brain abnormalities in both white and gray matter structures. However, the relationship between microstructural white matter differences and volumetric subcortical structures is not known. We investigated 30 long-term treated patients with schizophrenia and schizoaffective disorder (mean age 51.1 ± 7.9 years, mean illness duration 27.6 ± 8.0 years) and 42 healthy controls (mean age 54.1 ± 9.1 years) using 3 T diffusion and structural magnetic resonance imaging. The free-water imaging method was used to model the diffusion signal, and subcortical volumes were obtained from FreeSurfer. We applied multiple linear regression to investigate associations between (i) patient status and regional white matter microstructure, (ii) medication dose or clinical symptoms on white matter microstructure in patients, and (iii) for interactions between subcortical volumes and diagnosis on microstructural white matter regions showing significant patient-control differences. The patients had significantly decreased free-water corrected fractional anisotropy (FA_t), explained by decreased axial diffusivity and increased radial diffusivity (RD_t) bilaterally in the anterior corona radiata (ACR) and the left anterior limb of the internal capsule (ALIC) compared to controls. In the fornix, the patients had significantly increased RD_t. In patients, positive symptoms were associated with localized increased free-water and negative symptoms with localized decreased FA_t and increased RD_t. There were significant interactions between patient status and several subcortical structures on white matter microstructure and the free-water compartment for left ACR and fornix, and limited to the free-water compartment for right ACR and left ALIC. The Cohen's d effect sizes were medium to large (0.61 to 1.20, absolute values). The results suggest a specific pattern of frontal white matter axonal degeneration and demyelination and fornix demyelination that is attenuated in the presence of larger structures of the limbic system in patients with chronic schizophrenia and schizoaffective disorder. Findings warrant replication in larger samples.

Hippocampal subfield and amygdala nuclei volumes in schizophrenia patients with a history of violence

Schizophrenia (SCZ) is associated with an increased risk of violence compared to the general population. Previous studies have indicated smaller hippocampal and amygdala volumes in violent than non-violent psychotic patients^. However, little is known about volumetric differences at the subdivision level of these structures. In the present study, hippocampal subfields and amygdala nuclei volumes were estimated with FreeSurfer from 3 T MRI of SCZ patients with (SCZ-V, n = 24) and without (SCZ-NV, n = 51) a history of severe violence and 90 healthy controls (HC). Volumetric differences between groups were explored with a general linear model covarying for confounders, in addition to follow-up analyses in patient groups controlling for clinical characteristics such as antipsychotic medication, duration of illness and illicit substance use. SCZ-V had smaller total hippocampal volume and smaller CA1, HATA, fimbria, and molecular layer of DG volumes compared to HC. Total amygdala volume together with basal nucleus, accessory basal nucleus, CTA, and paralaminar nucleus volumes were smaller in SCZ-V compared to HC. In SCZ-NV, compared to HC, the observed smaller volumes were limited to basal and paralaminar nucleus. There were no significant differences in hippocampal subfield and amygdala nuclei volumes between SCZ-V and SCZ-NV. Follow-up analyses showed that the results in patient groups were not affected by clinical characteristics. The results suggest that smaller hippocampal subfield and amygdala nuclei volumes may be relevant to violence risk in SCZ. However, the neurobiological signature of violence in SCZ should be further investigated in larger cohorts.

Correlation Between Decreased Amygdala Subnuclei Volumes and Impaired Cognitive Functions in Pediatric Bipolar Disorder

BACKGROUND

The amygdala has been proposed to be involved in the pathophysiology of pediatric and adult bipolar disorder (BD). The goal of this structural magnetic resonance imaging (sMRI) study was to investigate the morphometric characteristics of amygdala subnuclei in patients with pediatric bipolar disorder (PBD) compared to healthy controls (HCs). Simultaneously, we examined correlation between amygdala subnuclei volumes and cognitive dysfunction.

MATERIALS AND METHODS

We assessed 40 adolescent outpatients, diagnosed with manic or euthymic PBD according to the DSM-5 criteria for BD and 19 HCs. Cognitive functions were evaluated using a Stroop color-word test (SCWT), trail making test (TMT), visual reproduction immediate recall subtest (VR I), and digit span subtest (DST). Amygdala and its subnuclei structures were automated segmented using FreeSurfer software and the volumes of them were compared between groups and correlation with clinical and cognitive outcomes was conducted.

RESULTS

Manic patients exhibited significantly decreased volumes in the bilateral whole amygdala and its basal nucleus, cortico-amygdaloid transition (CAT), and accessory basal nucleus (ABN) compared with HCs. Euthymic patients had decreased volume in the bilateral ABN and left CAT. In addition, we found significant positive associations between VR I scores and the right whole amygdala and its bilateral basal, right lateral, and ABN volumes in the manic group.

CONCLUSION

These findings support previous reports of smaller amygdala volumes and cognitive dysfunctions in PBD, and further mapping abnormalities to specific amygdala subnuclei. Correlation between basolateral volume and VR I of PBD may expand our understanding of neural abnormalities that could be targeted by treatment.

Amygdala Nuclei Volume and Shape in Military Veterans With Posttraumatic Stress Disorder

BACKGROUND

The amygdala is a subcortical structure involved in socioemotional and associative fear learning processes relevant for understanding the mechanisms of posttraumatic stress disorder (PTSD). Research in animals indicates that the amygdala is a heterogeneous structure in which the basolateral and centromedial divisions are susceptible to stress. While the amygdala complex is implicated in the pathophysiology of PTSD, little is known about the specific contributions of the individual nuclei that constitute the amygdala complex.

METHODS

Military veterans (n = 355), including military veterans with PTSD (n = 149) and trauma-exposed control subjects without PTSD (n = 206), underwent high-resolution T1-weighted anatomical scans. Automated FreeSurfer segmentation of the amygdala yielded 9 structures: basal, lateral, accessory basal, anterior amygdaloid, and central, medial, cortical, and paralaminar nuclei, along with the corticoamygdaloid transition zone. Subregional volumes were compared between groups using ordinary-least-squares regression with relevant demographic and clinical regressors followed by 3-dimensional shape analysis of whole amygdala.

RESULTS

PTSD was associated with smaller left and right lateral and paralaminar nuclei, but with larger left and right central, medial, and cortical nuclei (p < .05, false discovery rate corrected). Shape analyses revealed lower radial distance in anterior bilateral amygdala and lower Jacobian determinant in posterior bilateral amygdala in PTSD compared with control subjects.

CONCLUSIONS

Alterations in select amygdala subnuclear volumes and regional shape distortions are associated with PTSD in military veterans. Volume differences of the lateral nucleus and the centromedial complex associated with PTSD demonstrate a subregion-specific pattern that is consistent with their functional roles in fear learning and fear expression behaviors.

Higher resting-state BNST-CeA connectivity is associated with greater corrugator supercilii reactivity to negatively valenced images

The bed nucleus of the stria terminalis (BNST) and central nucleus of the amygdala (CeA) are hypothesized to be the output nodes of the extended amygdala threat response, integrating multiple signals to coordinate the threat response via outputs to the hypothalamus and brainstem. The BNST and CeA are structurally and functionally connected, suggesting interactions between these regions may regulate how the response to provocation unfolds. However, the relationship between human BNST-CeA connectivity and the behavioral response to affective stimuli is little understood. To investigate whether individual differences in BNST-CeA connectivity are related to the affective response to negatively valenced stimuli, we tested relations between resting-state BNST-CeA connectivity and both facial electromyographic (EMG) activity of the corrugator supercilii muscle and eyeblink startle magnitude during affective image presentation within the Refresher sample of the Midlife in the United States (MIDUS) study. We found that higher right BNST-CeA connectivity was associated with greater corrugator activity to negative, but not positive, images. There was a trend-level association between right BNST-CeA connectivity and trait negative affect. Eyeblink startle magnitude was not significantly related to BNST-CeA connectivity. These results suggest that functional interactions between BNST and CeA contribute to the behavioral response to negative emotional events.

Automated detection of hippocampal sclerosis using clinically empirical and radiomics features

OBJECTIVE

Temporal lobe epilepsy is a common form of epilepsy that might be amenable to surgery. However, magnetic resonance imaging (MRI)-negative hippocampal sclerosis (HS) can hamper early diagnosis and surgical intervention for patients in clinical practice, resulting in disease progression. Our aim was to automatically detect and evaluate the structural alterations of HS.

METHODS

Eighty patients with pharmacoresistant epilepsy and histologically proven HS and 80 healthy controls were included in the study. Two automated classifiers relying on clinically empirical and radiomics features were developed to detect HS. Cross-validation was implemented on all participants, and specificity was assessed in the 80 controls. The performance, robustness, and clinical utility of the model were also evaluated. Structural analysis was performed to investigate the morphological abnormalities of HS.

RESULTS

The computational model based on clinical empirical features showed excellent performance, with an area under the curve (AUC) of 0.981 in the primary cohort and 0.993 in the validation cohort. One of the features, gray-white matter boundary blurring in the temporal pole, exhibited the highest weight in model performance. Another model based on radiomics features also showed satisfactory performance, with AUC of 0.997 in the primary cohort and 0.978 in the validation cohort. In particular, the model improved the detection rate of MRI-negative HS to 96.0%. The novel feature of cortical folding complexity of the temporal pole not only played a crucial role in the classifier but also had significant correlation with disease duration.

SIGNIFICANCE

Machine learning with quantitative clinical and radiomics features is shown to improve HS detection. HS-related structural alterations were similar in the MRI-positive and MRI-negative HS patient groups, indicating that misdiagnosis originates mainly from empirical interpretation. The cortical folding complexity of the temporal pole is a potentially valuable feature for exploring the nature of HS.

Adverse Childhood Experiences and Amygdalar Reduction: High-Resolution Segmentation Reveals Associations With Subnuclei and Psychiatric Outcomes

The aim of the present study was 2-fold: (1) to utilize improved amygdala segmentation and exploratory factor analysis to characterize the latent volumetric structure among amygdala nuclei and (2) to assess the effect of adverse childhood experiences (ACEs) on amygdalar morphometry and current psychiatric symptoms. To investigate these aims, structural (T1) MRI and self-report data were obtained from 119 emerging adults. Regression analysis showed that higher ACE scores were related to reduced volume of the right, but not the left, amygdalar segments. Further, exploratory factor analysis yielded a two-factor structure, basolateral and central-medial nuclei of the right amygdala. Stractual equation modeling analyses revealed that higher ACE scores were significantly related to a reduced volume of the right basolateral and central-medial segments. Furthermore, reduction in the right basolateral amygdala was associated with increased anxiety, depressive symptoms, and alcohol use. This association supports an indirect effect between early adversity and psychiatric problems via reduced right basolateral amygdalar volume. The high-resolution segmentation results reveal a latent structure among amygdalar nuclei, which is consistent with prior work conducted in nonhuman mammals. These findings extend previous reports linking early adversity, right amygdala volume, and psychopathology.

Childhood Maltreatment, Cortical and Amygdala Morphometry, Functional Connectivity, Laterality, and Psychopathology

Child maltreatment (CM) is the most important preventable risk factor for psychopathology and there is a pressing need to understand how CM gets 'under the skin' to markedly increase risk in some individuals as well as a comparable effort to identify factors associated with better than expected outcomes in other individuals. This special issue of Child Maltreatment provides a series of sophisticated studies on the neurobiological impact of CM, of which we have chosen 4 articles to comment on.The articles by Oshri et al., and Peveril, Sheridan, Busso & McLaughlin are amygdala centric and provide important new information on the impact of CM on the morphology and functional connectivity of this highly stress susceptible structure. The article by Demers et al., presents data from a longitudinal study that illustrates the potentially disruptive effects of CM on the association between maternal relationship quality, frontal cortical development and symptomatology. Finally, the De Bellis et al., study addresses the pressing question, which we have labeled the 'ecophenotype hypothesis', that postulates that maltreated and non-maltreated individuals with the same primary DSM diagnosis are clinically and neurobiologically distinct, and provides new evidence for a specific prefrontal cortical neurobiological abnormality in the maltreated subtype.

Morphometric Biomarkers of Adolescents With Familial Risk for Alcohol Use Disorder

BACKGROUND

While many adolescents exhibit risky behavior, teenagers with a family history (FH+) of an alcohol use disorder (AUD) are at a heightened risk for earlier initiation of alcohol use, a more rapid escalation in frequency and quantity of alcohol consumption and developing a subsequent AUD in comparison with youth without such family history (FH-). Neuroanatomically, developmentally normative risk-taking behavior parallels an imbalance between more protracted development of the prefrontal cortex (PFC) and earlier development of limbic regions. Magnetic resonance imaging (MRI)-derived volumetric properties were obtained for these structures in FH+ and FH- adolescents.

METHODS

Forty-two substance-naïve adolescents (13- to 14-year-olds), stratified into FH+ (N = 19, 13 girls) and FH- (N = 23, 11 girls) age/handedness-matched groups, completed MRI scanning at 3.0T, as well as cognitive and clinical testing. T1 images were processed using FreeSurfer to measure PFC and hippocampi/amygdalae subfields/nuclei volumes.

RESULTS

FH+ status was associated with larger hippocampal/amygdala volumes (p < 0.05), relative to FH- adolescents, with right amygdala results appearing to be driven by FH+ boys. Volumetric differences also were positively associated with family history density (p < 0.05) of having an AUD. Larger subfields/nuclei volumes were associated with higher anxiety levels and worse auditory verbal learning performance (p < 0.05).

CONCLUSIONS

FH+ risk for AUD is detectable via neuromorphometric characteristics, which precede alcohol use onset and the potential onset of a later AUD, that are associated with emotional and cognitive measures. It is plausible that the development of limbic regions might be altered in FH+ youth, even prior to the onset of alcohol use, which could increase later risk. Thus, targeted preventative measures are warranted that serve to delay the onset of alcohol use in youth, particularly in those who are FH+ for an AUD.

Early diagnosis of Alzheimer's disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images

In recent years, several high-dimensional, accurate, and effective classification methods have been proposed for the automatic discrimination of the subject between Alzheimer's disease (AD) or its prodromal phase {i.e., mild cognitive impairment (MCI)} and healthy control (HC) persons based on T1-weighted structural magnetic resonance imaging (sMRI). These methods emphasis only on using the individual feature from sMRI images for the classification of AD, MCI, and HC subjects and their achieved classification accuracy is low. However, latest multimodal studies have shown that combining multiple features from different sMRI analysis techniques can improve the classification accuracy for these types of subjects. In this paper, we propose a novel classification technique that precisely distinguishes individuals with AD, aAD (stable MCI, who had not converted to AD within a 36-month time period), and mAD (MCI caused by AD, who had converted to AD within a 36-month time period) from HC individuals. The proposed method combines three different features extracted from structural MR (sMR) images using voxel-based morphometry (VBM), hippocampal volume (HV), and cortical and subcortical segmented region techniques. Three classification experiments were performed (AD vs. HC, aAD vs. mAD, and HC vs. mAD) with 326 subjects (171 elderly controls and 81 AD, 35 aAD, and 39 mAD patients). For the development and validation of the proposed classification method, we acquired the sMR images from the dataset of the National Research Center for Dementia (NRCD). A five-fold cross-validation technique was applied to find the optimal hyperparameters for the classifier, and the classification performance was compared by using three well-known classifiers: K-nearest neighbor, support vector machine, and random forest. Overall, the proposed model with the SVM classifier achieved the best performance on the NRCD dataset. For the individual feature, the VBM technique provided the best results followed by the HV technique. However, the use of combined features improved the classification accuracy and predictive power for the early classification of AD compared to the use of individual features. The most stable and reliable classification results were achieved when combining all extracted features. Additionally, to analyze the efficiency of the proposed model, we used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to compare the classification performance of the proposed model with those of several state-of-the-art methods.

The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables

Although covarying for potential confounds or nuisance variables is common in psychological research, relatively little is known about how the inclusion of covariates may influence the relations between psychological variables and indices of brain structure. In Part 1 of the current study, we conducted a descriptive review of relevant articles from the past two years of NeuroImage in order to identify the most commonly used covariates in work of this nature. Age, sex, and intracranial volume were found to be the most commonly used covariates, although the number of covariates used ranged from 0 to 14, with 37 different covariate sets across the 68 models tested. In Part 2, we used data from the Human Connectome Project to investigate the degree to which the addition of common covariates altered the relations between individual difference variables (i.e., personality traits, psychopathology, cognitive tasks) and regional gray matter volume (GMV), as well as the statistical significance of values associated with these effect sizes. Using traditional and random sampling approaches, our results varied widely, such that some covariate sets influenced the relations between the individual difference variables and GMV very little, while the addition of other covariate sets resulted in a substantially different pattern of results compared to models with no covariates. In sum, these results suggest that the use of covariates should be critically examined and discussed as part of the conversation on replicability in structural neuroimaging. We conclude by recommending that researchers pre-register their analytic strategy and present information on how relations differ based on the inclusion of covariates.

Subcortical Volume Correlates of Psychological Distress in Early Adolescence

The hippocampus and amygdala have justifiably been the focus of much mental health research due to their putative roles in top-down processing control of emotion, fear, and anxiety. However, understanding the causal relationship between these regions and mental illness has been limited as current literature is lacking in the observation of neuro-structural changes preceding first episodes. Here, we report whole and sub-structural hippocampal and amygdala volume correlates of psychological distress in early adolescence. Automated hippocampal subfield and amygdala nuclei segmentation was carried out in 32 participants (12-13 years old) recruited for the Longitudinal Adolescent Brain Study (LABS) who had psychological distress scores measured by the Kessler-10. Partial correlation analyses revealed significant negative association between left whole amygdala volume and psychological distress. Sub-structure analysis revealed that smaller left hippocampal CA1 volume and left basal and accessory basal amygdala nuclei volumes were all significantly associated with higher levels of psychological distress. Four-month follow-up analysis also revealed an association between change in K10 and CA1 volume suggesting a continued relationship between this hippocampal substructure and psychological distress. Grey matter volume of subcortical sub-structures involved within the hippocampal-basolateral amygdala-prefrontal cortex loop are highly correlated and are significantly reduced in adolescents with higher levels of psychological distress, indicating these nuclei and subfields play an important role in the emergence of mental illness.

Ultra-High-Resolution Imaging of Amygdala Subnuclei Structural Connectivity in Major Depressive Disorder

BACKGROUND

Major depressive disorder (MDD) is an increasingly common and disabling illness. As the amygdala has been reported to have pathological involvement in mood disorders, we aimed to investigate for the first time potential changes to structural connectivity of individual amygdala subnuclei in MDD using ultra-high-field 7T diffusion magnetic resonance imaging.

METHODS

Twenty-four patients with MDD (11 women) and 24 age-matched healthy control participants (7 women) underwent diffusion-weighted imaging with a 1.05-mm isotropic resolution at 7T. Amygdala nuclei regions of interest were obtained through automated segmentation of 0.69-mm resolution T1-weighted images and 0.35-mm resolution T2-weighted images. Probabilistic tractography was performed on all subjects, with random seeding at each amygdala nucleus.

RESULTS

The right lateral, basal, central, and centrocortical amygdala nuclei exhibited significantly increased connection density to the rest of the brain, whereas the left medial nucleus demonstrated significantly lower connection density (false discovery rate p < .05). Increased connection density in the right lateral and basal nuclei was driven by the stria terminalis, and the significant difference in the right central nucleus was driven by the uncinate fasciculus. Decreased connection density at the left medial nucleus did not appear to be driven by any individual white matter tract.

CONCLUSIONS

By exploiting ultra-high-resolution magnetic resonance imaging, structural hyperconnectivity was demonstrated involving the amygdaloid nuclei in the right hemisphere in MDD. To a lesser extent, impairment of subnuclei connectivity was shown in the left hemisphere.

An evaluation of automated tracing for orbitofrontal cortex sulcogyral pattern typing

BACKGROUND

Characterization of stereotyped orbitofrontal cortex (OFC) sulcogyral patterns formed by the medial and lateral orbitofrontal sulci (MOS and LOS) can be used to characterize individual variability; however, in practice, issues exist for reliability and reproducibility of anatomical classifications, as current methods rely on manual tracing.

NEW METHOD

We assessed whether an automated tracing procedure would be useful for characterizing OFC sulcogyral patterns. 100 subjects from a published collection of manual OFC tracings and characterizations of patients with bipolar disorder, schizophrenia, and typical controls were used to evaluate an automated tracing procedure implemented using the BrainVISA Morphologist Pipeline.

RESULTS

Automated tracings of caudal and rostral segments of the medial (MOSc/MOSr) and lateral (LOSc/LOSr) orbitofrontal sulci, as well as the intermediate (IOS) and transverse orbitofrontal sulci (TOS) were found to accurately identify OFC sulci, accurately portray sulci continuity, and reliably inform manual sulcogyral pattern characterization.

COMPARISON WITH EXISTING METHOD

Automated tracings produced visibly similar tracings of OFC sulci and removed subjective influence from locating sulci. The semi-automated pipeline of automated tracing and manual sulcogyral pattern characterization can eliminate the need for direct input during the most time-consuming process of the manual pipeline.

CONCLUSIONS

The results suggest that automated OFC sulci tracing methods using BrainVISA Morphologist are feasible and useful in a semi-automated pipeline to characterize OFC sulcogyral patterns. Automated OFC sulci tracing methods will improve reliability and reproducibility of sulcogyral characterizations and can allow for characterizations of sulcal patterns types in larger sample sizes, previously unattainable using traditional manual tracing procedures.