Hippocampal Subfield Segmentation Research Articles

25 High-resolution MRI Reveals Selective Patterns of Hippocampal Subfield Atrophy in Focal Epilepsy

Objective:Hippocampal pathology is a consistent feature in persons with temporal lobe epilepsy (TLE) and a strong biomarker of memory impairment. Histopathological studies have identified selective patterns of cell loss across hippocampal subfields in TLE, the most common being cellular loss in the cornu ammonis 1 (CA1) and dentage gyrus (DG). Structural neuroimaging provides a non-invasive method to understand hippocampal pathology, but traditionally only at a whole-hippocampal level. However, recent methodological advances have enabled the non-invasive quantification of subfield pathology in patients, enabling potential integration into clinical workflow. In this study, we characterize patterns of hippocampal subfield atrophy in patients with TLE and examine the associations between subfield atrophy and clinical characteristics.Participants and Methods:High-resolution T2 and T1-weighted MRI were collected from 31 participants (14 left TLE; 6 right TLE; 11 healthy controls [HC], aged 18-61 years). Reconstructions of hippocampal subfields and estimates of their volumes were derived using the Automated Segmentation of Hippocampal Subfields (ASHS) pipeline. Total hippocampal volume was calculated by combining estimates of the subfields CA1-3, DG, and subiculum. To control for variations in head size, all volume estimates were divided by estimates of total brain volume. To assess disease effects on hippocampal atrophy, hippocampi were recoded as either ipsilateral or contralateral to the side of seizure focus. Two sample t-tests at a whole-hippocampus level were used to test for ipsilateral and contralateral volume loss in patients relative to HC. To assess whether we replicated the selective histopathological patterns of subfield atrophy, we carried out mixed-effects ANOVA, coding for an interaction between diagnostic group and hippocampal subfield. Finally, to assess effects of disease load, non-parametric correlations were performed between subfield volume and age of first seizure and duration of illness.Results:Patients had significantly smaller total ipsilateral hippocampal volume compared with HC (d=1.23, p<.005). Contralateral hippocampus did not significantly differ between TLE and HC. Examining individual subfields for the ipsilateral hemisphere revealed significant main-effects for group (F(1, 29)=8.2, p<0.01), subfields (F(4, 115)=550.5, p<0.005), and their interaction (F(4, 115)=8.1, p<0.001). Post-hoc tests revealed that TLE had significantly smaller volume in the ipsilateral CA1 (d=-2.0, p<0.001) and DG (d = -1.4, p<0.005). Longer duration of illness was associated with smaller volume of ipsilateral CA2 (p=-0.492, p<0.05) and larger volume of contralateral whole-hippocampus (p=0.689, p<0.001), CA1 (p=0.614, p < 0.005), and DG (p=0.450, p<0.05).Conclusions:Histopathological characterization after surgery has revealed important associations between hippocampal subfield cell loss and memory impairments in patients with TLE. Here we demonstrate that non-invasive neuroimaging can detect a pattern of subfield atrophy in TLE (i.e., CA1/DG) that matches the most common form of histopathologically-observed hippocampal sclerosis in TLE (HS Type 1) and has been linked directly to both verbal and visuospatial memory impairment. Finally, we found evidence that longer disease duration is associated with larger contralateral hippocampal volume, driven by increases in CA1 and DG. This may reflect subfield-specific functional reorganization to the unaffected brain tissue, a compensatory effect which may have important implications for patient function and successful treatment outcomes.

The volume of the thalamus and hippocampus in a right-handed female episodic migraine group

Background/aimMigraine is a disabling headache with clinical and radiological complications. The aim of this study was to investigate the volume of the thalamus and hippocampus in migraineurs, the role of white matter lesions (WMLs), and the migraine characteristics in volume changes.MethodsBrain MRIs of 161 right-handed female episodic migraine patients and 40 right-handed, age-related, healthy women were performed. Left and right thalamus segmentation was performed on the 3D MPRAGE images using the Freesurfer 5.3 image analysis suite. Hippocampal subfield segmentation was based on a novel statistical atlas built primarily upon ultra-high-resolution ex vivo MRI data.ResultsThe left hippocampus had a smaller and the left thalamus had a larger total volume than the right one in both the control (p < 0.001) and migraine groups (p <0.001). Patients with white matter lesions (L+) showed smaller right thalamus and right hippocampal tail volumes than patients without lesions (L–) (p = 0.002 and p = 0.015, respectively) and controls (p = 0.039 and p = 0.025, respectively). For the right hippocampal body, we found significantly smaller volume in L+ patients when compared to L– patients (p = 0.018) and a similar trend when compared to the control group (p = 0.064). Patients without aura (A–) showed a larger right hippocampus (p = 0.029), right hippocampal body (p = 0.012), and tail volumes (p = 0.011) than patients with aura (A+). Inverse correlations were found between attack frequency and the volumes of the left and right hippocampal tails (p = 0.018 and p = 0.008, respectively).ConclusionThese findings indicate that WMLs may influence the volume of the right thalamus and hippocampus, while migraine aura and attack frequency may lead to volume changes in different parts of the hippocampi in migraine patients. These data support the necessity of effective migraine management to limit subcortical volume loss in migraineurs.

Syn_SegNet: A Joint Deep Neural Network for Ultrahigh-Field 7T MRI Synthesis and Hippocampal Subfield Segmentation in Routine 3T MRI.

Precise delineation of hippocampus subfields is crucial for the identification and management of various neurological and psychiatric disorders. However, segmenting these subfields automatically in routine 3T MRI is challenging due to their complex morphology and small size, as well as the limited signal contrast and resolution of the 3T images. This research proposes Syn_SegNet, an end-to-end, multitask joint deep neural network that leverages ultrahigh-field 7T MRI synthesis to improve hippocampal subfield segmentation in 3T MRI. Our approach involves two key components. First, we employ a modified Pix2PixGAN as the synthesis model, incorporating self-attention modules, image and feature matching loss, and ROI loss to generate high-quality 7T-like MRI around the hippocampal region. Second, we utilize a variant of 3D-U-Net with multiscale deep supervision as the segmentation subnetwork, incorporating an anatomic weighted cross-entropy loss that capitalizes on prior anatomical knowledge. We evaluate our method on hippocampal subfield segmentation in paired 3T MRI and 7T MRI with seven different anatomical structures. The experimental findings demonstrate that Syn_SegNet's segmentation performance benefits from integrating synthetic 7T data in an online manner and is superior to competing methods. Furthermore, we assess the generalizability of the proposed approach using a publicly accessible 3T MRI dataset. The developed method would be an efficient tool for segmenting hippocampal subfields in routine clinical 3T MRI.

Validating ASHS-T1 automated entorhinal and transentorhinal cortical segmentation in Alzheimer's disease

The current study aimed to validate entorhinal and transentorhinal cortical volumes measured by the automated segmentation tool Automatic Segmentation of Hippocampal Subfields (ASHS-T1). The study sample comprised 34 healthy controls (HCs), 37 individuals with amnestic mild cognitive impairment (aMCI), and 29 individuals with Alzheimer's disease (AD) dementia from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Entorhinal and transentorhinal cortical volumes were assessed using ASHS-T1, manual segmentation, as well as a widely used automated segmentation tool, FreeSurfer v6.0.1. Mean differences, intraclass correlation coefficients, and Bland–Altman plots were computed. ASHS-T1 tended to underestimate entorhinal and transentorhinal cortical volumes relative to manual segmentation and FreeSurfer. There was variable consistency and low agreement between ASHS-T1 and manual segmentation volumes. There was low-to-moderate consistency and low agreement between ASHS-T1 and FreeSurfer volumes. There was a trend toward higher consistency and agreement for the entorhinal cortex in the aMCI and AD groups compared to the HC group. Despite the differences in volume measurements, ASHS-T1 was sensitive to entorhinal and transentorhinal cortical atrophy in both early and late disease stages. Based on the current study, ASHS-T1 appears to be a promising tool for automated entorhinal and transentorhinal cortical volume measurement in individuals with likely underlying AD.

Release of cognitive and multimodal MRI data including real-world tasks and hippocampal subfield segmentations

We share data from N = 217 healthy adults (mean age 29 years, range 20–41; 109 females, 108 males) who underwent extensive cognitive assessment and neuroimaging to examine the neural basis of individual differences, with a particular focus on a brain structure called the hippocampus. Cognitive data were collected using a wide array of questionnaires, naturalistic tests that examined imagination, autobiographical memory recall and spatial navigation, traditional laboratory-based tests such as recalling word pairs, and comprehensive characterisation of the strategies used to perform the cognitive tests. 3 Tesla MRI data were also acquired and include multi-parameter mapping to examine tissue microstructure, diffusion-weighted MRI, T2-weighted high-resolution partial volume structural MRI scans (with the masks of hippocampal subfields manually segmented from these scans), whole brain resting state functional MRI scans and partial volume high resolution resting state functional MRI scans. This rich dataset will be of value to cognitive and clinical neuroscientists researching individual differences, real-world cognition, brain-behaviour associations, hippocampal subfields and more. All data are freely available on Dryad.

Medial Temporal Lobe Atrophy in Predementia Alzheimer's Disease: A Longitudinal Multi-Site Study Comparing Staging and A/T/N in a Clinical Research Cohort.

Atrophy of the medial temporal lobe (MTL) is a biological characteristic of Alzheimer's disease (AD) and can be measured by segmentation of magnetic resonance images (MRI). To assess the clinical utility of automated volumetry in a cognitively well-defined and biomarker-classified multi-center longitudinal predementia cohort. We used Automatic Segmentation of Hippocampal Subfields (ASHS) to determine MTL morphometry from MRI. We harmonized scanner effects using the recently developed longitudinal ComBat. Subjects were classified according to the A/T/N system, and as normal controls (NC), subjective cognitive decline (SCD), or mild cognitive impairment (MCI). Positive or negative values of A, T, and N were determined by cerebrospinal fluid measurements of the Aβ42/40 ratio, phosphorylated and total tau. From 406 included subjects, longitudinal data was available for 206 subjects by stage, and 212 subjects by A/T/N. Compared to A-/T-/N- at baseline, the entorhinal cortex, anterior and posterior hippocampus were smaller in A+/T+orN+. Compared to NC A- at baseline, these subregions were also smaller in MCI A+. Longitudinally, SCD A+ and MCI A+, and A+/T-/N- and A+/T+orN+, had significantly greater atrophy compared to controls in both anterior and posterior hippocampus. In the entorhinal and parahippocampal cortices, longitudinal atrophy was observed only in MCI A+ compared to NC A-, and in A+/T-/N- and A+/T+orN+ compared to A-/T-/N-. We found MTL neurodegeneration largely consistent with existing models, suggesting that harmonized MRI volumetry may be used under conditions that are common in clinical multi-center cohorts.

Hippocampal Subfields, Daily Stressors, and Resting Cortisol in Individuals at Clinical High-Risk for Psychosis

The hippocampus, comprised of functionally distinct subfields, both regulates stress and is affected by it during psychosis pathogenesis. Hippocampal abnormalities are evident across psychosis spectrum and are associated with aberrant cortisol levels and greater environmental stressors exposure. These associations, particularly at the subfield-level, are poorly understood in individuals at clinical high-risk (CHR) for psychosis- a population characterized by an interplay between environmental stressors and biological susceptibility. A total of 121 participants including 51 CHR (mean age=18.61) and 70 healthy controls (HC; mean age=18.3) were enrolled. Participants completed a structural scan, salivary cortisol assays, and a self-report measure assessing distress from daily stressors exposure (DSI). Hippocampal subfield segmentation was conducted using Freesurfer. Smaller hippocampal subfields were associated with greater stress levels. Greater DSI was associated with lower volumes in CA1 (r=-.38) and CA2/3 (r=-.29), but not in CA4/DG (r=-.28), presubiculum (r=-.09), or subiculum (r=-.17). Higher resting cortisol was associated with lower volumes in presubiculum (r=-.4) but not subiculum (r=-.22), CA1 (r=.08), CA2/3 (r=.1), or CA4/DG (r=-.005). Regressions indicated effects for CA1 and DSI (b=.57, p=.03) and presubiculum and cortisol (b=.61, p=.02) are specific to CHR participants relative to HCs. The findings provided insights into links between stress and brain vulnerability during psychosis-risk period. Regional differences highlighted potentially different mechanisms by which stress impacts specific subfields. Presubiculum may be more susceptible to the impact of early stress on HPA-axis and cornu amonis to acute stressors.

Hippocampal subfields’ sex differences in EOAD

AbstractBackgroundIn healthy ageing, there is evidence of sex differences in vulnerability of hippocampal subfields to volume loss. However, this has not been investigated in early‐onset Alzheimer’s disease (<65 years; EOAD).MethodWe included 106 subjects: 62 EOAD (A+T+N+, MMSE>15) and 44 healthy controls (HC; A‐T‐N‐) that underwent lumbar puncture for analysis of AD biomarkers, 3T‐MRI scan and neuropsychological assessment. Hippocampal subfield segmentation was performed using T1‐weighted images and Freesurfer 6.0. Volume was adjusted by intracranial volume. Adjusted linear models were used to analyze differences between EOAD and HC and differences between sexes. We calculated Cohen’s d as a measure of the effect size of volume change by sex, restricted to volume differences between EOAD and HC. In EOAD, we used linear models adjusted by age and education to investigate the association of volume loss with 18 cognition z‐scores. Results were adjusted using Bonferroni correction for multiple comparisons.ResultThere were no demographic differences across groups. APOEε4 carriers were higher in EOAD‐female/EOAD‐male than HC‐female. EOAD‐female showed higher T‐Tau and P‐Tau levels than EOAD‐male (all p<0.05; Table1). Comparing EOAD vs. HC, differences were found in volume of all subfields and hippocampus (p<0.05, Bonferroni corrected), except for bilateral parasubiculum and right cornu ammonis (CA) 2/3. No differences were found between EOAD‐female and EOAD‐male (p>0.05). When compared to HC of the same sex, EOAD‐female showed differences in the same regions as the whole sample, while EOAD‐male showed differences in bilateral hippocampal tail and left presubiculum and hippocampus (all p<0.05, Bonferroni corrected, Figure 1). We observed larger effect sizes than in women in these regions, except for left hippocampus (Figure 2).In EOAD, higher volume in left CA2/3 predicted higher memory z‐scores, including free and total learning and delayed total recall (p<0.05 Bonferroni corrected; Figure 3). No associations were found in EOAD‐female nor EOAD‐male.ConclusionIn EOAD, the pattern of volume loss in hippocampal subfields was similar between sexes. However, females showed more marked differences from HC, except for bilateral hippocampal tail and left presubiculum. Further studies are necessary to elucidate the existence of sex differences in EOAD and their implication for cognitive impairment.

Development and validation of a quality control procedure for automatic segmentation of hippocampal subfields.

Automatic segmentation methods for in vivo magnetic resonance imaging are increasing in popularity because of their high efficiency and reproducibility. However, automatic methods can be perfectly reliable and consistently wrong, and the validity of automatic segmentation methods cannot be taken for granted. Quality control (QC) by trained and reliable human raters is necessary to ensure the validity of automatic measurements. Yet QC practices for applied neuroimaging research are underdeveloped. We report a detailed QC and correction procedure to accompany our validated atlas for hippocampal subfield segmentation. We document a two-step QC procedure for identifying segmentation errors, along with a taxonomy of errors and an error severity rating scale. This detailed procedure has high between-rater reliability for error identification and manual correction. The latter introduces at maximum 3% error variance in volume measurement. All procedures were cross-validated on an independent sample collected at a second site with different imaging parameters. The analysis of error frequency revealed no evidence of bias. An independent rater with a third sample replicated procedures with high within-rater reliability for error identification and correction. We provide recommendations for implementing the described method along with hypothesis testing strategies. In sum, we present a detailed QC procedure that is optimized for efficiency while prioritizing measurement validity and suits any automatic atlas.

An automated, geometry-based method for hippocampal shape and thickness analysis

The hippocampus is one of the most studied neuroanatomical structures due to its involvement in attention, learning, and memory as well as its atrophy in ageing, neurological, and psychiatric diseases. Hippocampal shape changes, however, are complex and cannot be fully characterized by a single summary metric such as hippocampal volume as determined from MR images. In this work, we propose an automated, geometry-based approach for the unfolding, point-wise correspondence, and local analysis of hippocampal shape features such as thickness and curvature. Starting from an automated segmentation of hippocampal subfields, we create a 3D tetrahedral mesh model as well as a 3D intrinsic coordinate system of the hippocampal body. From this coordinate system, we derive local curvature and thickness estimates as well as a 2D sheet for hippocampal unfolding. We evaluate the performance of our algorithm with a series of experiments to quantify neurodegenerative changes in Mild Cognitive Impairment and Alzheimer’s disease dementia. We find that hippocampal thickness estimates detect known differences between clinical groups and can determine the location of these effects on the hippocampal sheet. Further, thickness estimates improve classification of clinical groups and cognitively unimpaired controls when added as an additional predictor. Comparable results are obtained with different datasets and segmentation algorithms. Taken together, we replicate canonical findings on hippocampal volume/shape changes in dementia, extend them by gaining insight into their spatial localization on the hippocampal sheet, and provide additional, complementary information beyond traditional measures. We provide a new set of sensitive processing and analysis tools for the analysis of hippocampal geometry that allows comparisons across studies without relying on image registration or requiring manual intervention.

Postprocessing of MRIs Using FreeSurfer in Epilepsy Surgery Patients Provides an Excellent Imaging Marker of Hippocampal Sclerosis but Fails to Separate Subtypes

Objective. Histopathological examinations will diminish as minimally invasive epilepsy surgery increasingly replaces open surgery. The objective of this study was to test if visual and computer-aided quantitative analyses of presurgical high-quality 3 Tesla MRIs complying with the International League Against Epilepsy (ILAE) Neuroimaging Task Force recommendations can inform on histopathological diagnosis. Methods. Ninety-two patients from Copenhagen and Oslo University Hospitals fulfilled patient-, imaging-, and histopathological inclusion criteria: 69 patients were diagnosed with hippocampal sclerosis (HS) ILAE type 1 or 2, and 23 patients had normal appearing hippocampi or other histopathology than HS (no-HS). MRIs from 52 healthy controls (HC) were included. Image processing was performed in FreeSurfer v.6.0 with the built-in cross-sectional hippocampal subfield segmentation tool and multimodal MRI input. Volume outputs were used to calculate volume asymmetry ratios (VARs) for whole hippocampus (WH) and subfields. Results. HS patients had significantly larger WH VARs compared to no-HS patients and HC, with a sensitivity = 0.93 and specificity = 1.0 for histopathological HS diagnosis. Visual MRI assessment yielded a sensitivity = 0.90 and specificity = 0.96 for histopathological HS diagnosis. CA1 and CA4 VARs and the number of seizure-free patients were not significantly different in HS ILAE type 1 compared to type 2 patients. Significance. FreeSurfer analyses of presurgical MRIs are excellent at separating patients histopathologically diagnosed with HS from patients with other pathology or normal appearing hippocampi. Using the FreeSurfer hippocampal subfield segmentation tool did not allow for separating HS ILAE subtypes.

Plasma concentrations of anti-inflammatory cytokine TGF-β are associated with hippocampal structure related to explicit memory performance in older adults

Human cognitive abilities, and particularly hippocampus-dependent memory performance typically decline with increasing age. Immunosenescence, the age-related disintegration of the immune system, is increasingly coming into the focus of research as a considerable factor contributing to cognitive decline. In the present study, we investigated potential associations between plasma levels of pro- and anti-inflammatory cytokines and learning and memory performance as well as hippocampal anatomy in young and older adults. Plasma concentrations of the inflammation marker CRP as well as the pro-inflammatory cytokines IL-6 and TNF-α and the anti-inflammatory cytokine TGF-β1 were measured in 142 healthy adults (57 young, 24.47 ± 4.48 years; 85 older, 63.66 ± 7.32 years) who performed tests of explicit memory (Verbal Learning and Memory Test, VLMT; Wechsler Memory Scale, Logical Memory, WMS) with an additional delayed recall test after 24 h. Hippocampal volumetry and hippocampal subfield segmentation were performed using FreeSurfer, based on T1-weighted and high-resolution T2-weighted MR images. When investigating the relationship between memory performance, hippocampal structure, and plasma cytokine levels, we found that TGF-β1 concentrations were positively correlated with the volumes of the hippocampal CA4–dentate gyrus region in older adults. These volumes were in turn positively associated with better performance in the WMS, particularly in the delayed memory test. Our results support the notion that endogenous anti-inflammatory mechanisms may act as protective factors in neurocognitive aging.

Volume changes of hippocampal and amygdala subfields in patients with mild cognitive impairment and Alzheimer's disease.

Automated segmentation of hippocampal and amygdala subfields could improve classification accuracy of Mild Cognitive Impairments (MCI) and Alzheimer's Disease (AD) individuals. We applied T1-weighted magnetic resonance imaging (MRI) for 21 AD, 39 MCI and 32 normalcontrol(NC) participants at 3-Tesla MRI. Twelve hippocampal subfields and 9 amygdala subfields in each hemisphere were analyzed using FreeSurfer 6.0. Smaller volumes were observed in right/left whole hippocampus, right/left hippocampal tail, right/left subiculum, right Cornu ammonis 1(CA1), right/left molecular layer, right granule cell-molecular layer-dentate gyrus (GC-ML-DG), right CA4, right fimbria, right whole amygdala, right/left accessory basal, right anterior amygdala area, left central, left medial and right/left cortical nucleus of AD group compared to both MCI and NC groups (p < 0.001). The volumes of right presubiculum, right CA3, right hippocampus-amygdala-transition-area (HATA), right lateral, right basal, right central, right medial, right cortico-amygdaloid transition (CAT) and right paralaminar nucleus were significantly larger in NC than AD group (p ≤ 0.001), while the volumes of right subiculum, right CA1, right molecular layer, right whole hippocampus, right whole amygdala, right basal and right accessory basal were significantly larger in NC than MCI group (p ≤ 0.002). Trend analysis showed that most hippocampus and amygdala subfields have a trend of atrophy with the decline of cognitive function. Six core components were identified by the hierarchical clustering. The combined Receiver operating characteristic (ROC) analysis achieved the diagnostic performances (AUC: 0.81) in differentiating AD from MCI; (AUC: 0.79) in differentiating MCI from NC and (AUC: 0.97) in differentiating AD from NC. Volumetric differences of hippocampus and amygdala were at a finer subfields scale, and the volumes of right basal nucleus, left parasubiculum, left medial nucleus, left GC-ML-DG, left hippocampal fissure, and right fimbria can be employed as neuroimaging biomarkers to assist the clinical diagnosis of MCI and AD.

Quantifying numerical and spatial reliability of hippocampal and amygdala subdivisions in FreeSurfer

On-going, large-scale neuroimaging initiatives can aid in uncovering neurobiological causes and correlates of poor mental health, disease pathology, and many other important conditions. As projects grow in scale with hundreds, even thousands, of individual participants and scans collected, quantification of brain structures by automated algorithms is becoming the only truly tractable approach. Here, we assessed the spatial and numerical reliability for newly deployed automated segmentation of hippocampal subfields and amygdala nuclei in FreeSurfer 7. In a sample of participants with repeated structural imaging scans (N = 928), we found numerical reliability (as assessed by intraclass correlations, ICCs) was reasonable. Approximately 95% of hippocampal subfields had “excellent” numerical reliability (ICCs ≥ 0.90), while only 67% of amygdala subnuclei met this same threshold. In terms of spatial reliability, 58% of hippocampal subfields and 44% of amygdala subnuclei had Dice coefficients ≥ 0.70. Notably, multiple regions had poor numerical and/or spatial reliability. We also examined correlations between spatial reliability and person-level factors (e.g., participant age; T1 image quality). Both sex and image scan quality were related to variations in spatial reliability metrics. Examined collectively, our work suggests caution should be exercised for a few hippocampal subfields and amygdala nuclei with more variable reliability.Graphical

Pentad: A reproducible cytoarchitectonic protocol and its application to parcellation of the human hippocampus.

The hippocampus is integral for learning and memory and is targeted by multiple diseases. Neuroimaging approaches frequently use hippocampal subfield volumes as a standard measure of neurodegeneration, thus making them an essential biomarker to study. Collectively, histologic parcellation studies contain various disagreements, discrepancies, and omissions. The present study aimed to advance the hippocampal subfield segmentation field by establishing the first histology based parcellation protocol, applied to n = 22 human hippocampal samples. The protocol focuses on five cellular traits observed in the pyramidal layer of the human hippocampus. We coin this approach the pentad protocol. The traits were: chromophilia, neuron size, packing density, clustering, and collinearity. Subfields included were CA1, CA2, CA3, CA4, prosubiculum, subiculum, presubiculum, parasubiculum, as well as the medial (uncal) subfields Subu, CA1u, CA2u, CA3u, and CA4u. We also establish nine distinct anterior-posterior levels of the hippocampus in the coronal plane to document rostrocaudal differences. Applying the pentad protocol, we parcellated 13 subfields at nine levels in 22 samples. We found that CA1 had the smallest neurons, CA2 showed high neuronal clustering, and CA3 displayed the most collinear neurons of the CA fields. The border between presubiculum and subiculum was staircase shaped, and parasubiculum had larger neurons than presubiculum. We also demonstrate cytoarchitectural evidence that CA4 and prosubiculum exist as individual subfields. This protocol is comprehensive, regimented and supplies a high number of samples, hippocampal subfields, and anterior-posterior coronal levels. The pentad protocol utilizes the gold standard approach for the human hippocampus subfield parcellation.

Stress-related reduction of hippocampal subfield volumes in major depressive disorder: A 7-Tesla study.

Major depressive disorder (MDD) is a prevalent health problem with complex pathophysiology that is not clearly understood. Prior work has implicated the hippocampus in MDD, but how hippocampal subfields influence or are affected by MDD requires further characterization with high-resolution data. This will help ascertain the accuracy and reproducibility of previous subfield findings in depression as well as correlate subfield volumes with MDD symptom scores. The objective of this study was to assess volumetric differences in hippocampal subfields between MDD patients globally and healthy controls (HC) as well as between a subset of treatment-resistant depression (TRD) patients and HC using automatic segmentation of hippocampal subfields (ASHS) software and ultra-high field MRI. Thirty-five MDD patients and 28 HC underwent imaging using 7-Tesla MRI. ASHS software was applied to the imaging data to perform automated hippocampal segmentation and provide volumetrics for analysis. An exploratory analysis was also performed on associations between symptom scores for diagnostic testing and hippocampal subfield volumes. Compared to HC, MDD and TRD patients showed reduced right-hemisphere CA2/3 subfield volume (p = 0.01, η 2 = 0.31 and p = 0.3, η 2 = 0.44, respectively). Additionally, negative associations were found between subfield volumes and life-stressor checklist scores, including left CA1 (p = 0.041, f 2 = 0.419), left CA4/DG (p = 0.010, f 2 = 0.584), right subiculum total (p = 0.038, f 2 = 0.354), left hippocampus total (p = 0.015, f 2 = 0.134), and right hippocampus total (p = 0.034, f 2 = 0.110). Caution should be exercised in interpreting these results due to the small sample size and low power. Determining biomarkers for MDD and TRD pathophysiology through segmentation on high-resolution MRI data and understanding the effects of stress on these regions can enable better assessment of biological response to treatment selection and may elucidate the underlying mechanisms of depression.

Automated and manual segmentation of the hippocampus in human infants

The hippocampus, critical for learning and memory, undergoes substantial changes early in life. Investigating the developmental trajectory of hippocampal structure and function requires an accurate method for segmenting this region from anatomical MRI scans. Although manual segmentation is regarded as the “gold standard” approach, it is laborious and subjective. This has fueled the pursuit of automated segmentation methods in adults. However, little is known about the reliability of these automated protocols in infants, particularly when anatomical scan quality is degraded by head motion or the use of shorter and quieter infant-friendly sequences. During a task-based fMRI protocol, we collected quiet T1-weighted anatomical scans from 42 sessions with awake infants aged 4–23 months. Two expert tracers first segmented the hippocampus in both hemispheres manually. The resulting inter-rater reliability (IRR) was only moderate, reflecting the difficulty of infant segmentation. We then used four protocols to predict these manual segmentations: average adult template, average infant template, FreeSurfer software, and Automated Segmentation of Hippocampal Subfields (ASHS) software. ASHS generated the most reliable hippocampal segmentations in infants, exceeding the manual IRR of experts. Automated methods thus provide robust hippocampal segmentations of noisy T1-weighted infant scans, opening new possibilities for interrogating early hippocampal development.

Hippocampal subfield volumetry in corticobasal syndrome of diverse underlying pathologies.

Multiple pathologies may underlie corticobasal syndrome (CBS), including Alzheimer's disease (AD). Typical amnestic AD is characterized by early selective hippocampal atrophy. The profile of hippocampal atrophy in AD patients presenting as CBS (CBS-AD), compared to CBS patients of non-AD pathologies (CBS-nAD) and amnestic AD patients, has not been studied. To compare hippocampal subfield atrophy patterns between CBS-AD, CBS-nAD, typical amnestic AD patients, and control subjects. Automated hippocampal subfield volumetry was performed via the hippocampal subfield segmentation pipeline of Freesurfer 6.0 on 3D T1-weighted images. CBS patients were classified as CBS-AD or CBS-nAD based on CSF AD biomarkers by applying the AT(N) classification system. Mean volumes of nine hippocampal subfields, head-body-tail segments, total hippocampus, and entorhinal and parahippocampal gyrus cortical thickness were measured. Eighty-three subjects were included (CBS-AD: n = 14; CBS-nAD: n = 17; amnestic AD: n = 29; controls: n = 23). CBS-AD patients had greater whole hippocampal and hippocampal subfield atrophy compared to CBS-nAD. CBS-AD and amnestic AD patients did not differ in subfield volumes. CBS-nAD did not exhibit hippocampal atrophy compared to controls, with the exception of fimbria. (Cohen's d = 1.27; p = 0.038). Presubiculum (Cohen's d = 1.00; p = 0.002) and hippocampal body (Cohen's d = 0.95; p = 0.001) volumes exhibited the greatest differences between CBS-AD and CBS-nAD. Hippocampal subfield volume provided combined sensitivity and specificity < 80% for the discrimination of CBS-AD from CBS-nAD. CBS-AD and amnestic AD patients exhibit comparable, and significantly greater hippocampal atrophy compared to CBS-nAD patients. Hippocampal subfield volumetry in CBS is indicative of an AD underlying pathology.

Hippocampal subfields, daily stressors, and resting cortisol in individuals at clinical high-risk for psychosis

IntroductionThe hippocampus, comprised of functionally distinct subfields, both regulates stress and is affected by it during psychosis pathogenesis. Hippocampal abnormalities are evident across psychosis spectrum and are associated with aberrant cortisol levels and greater environmental stressors exposure. These associations, particularly at the subfield-level, are poorly understood in individuals at clinical high-risk (CHR) for psychosis. This represents a significant literature gap given this critical pathogenetic period is characterized by an interplay between environmental stressors and biological susceptibility. MethodsA total of 121 participants including 51 CHR (mean age=18.61) and 70 healthy controls (HC; mean age=18.3) were enrolled in the study. Participants completed a structural scan, salivary cortisol assays, and a self-report measure assessing distress from daily stressors exposure (DSI). Hippocampal subfield segmentation was conducted using Freesurfer. ResultsSmaller hippocampal subfields were associated with greater stress levels. Greater DSI was associated with lower volumes in CA1 (r = −0.38) and CA2/3 (r = −0.29), but not in CA4/DG (r = −0.28), presubiculum (r = −0.09), or subiculum (r = −0.17). Higher resting cortisol was associated with lower volumes in presubiculum (r = −0.4) but not subiculum (r = −0.22), CA1 (r = 0.08), CA2/3 (r = 0.1), or CA4/DG (r = −0.005). Regressions indicated effects for CA1 and DSI (β = 0.57, p = .03) and presubiculum and cortisol (β = 0.61, p = .02) are specific to CHR participants relative to HCs. ConclusionsThe findings provided insights into links between stress and brain vulnerability during psychosis-risk period. Regional differences highlighted potentially different mechanisms by which stress impacts specific subfields. Presubiculum may be more susceptible to the impact of early stress on HPA-axis and cornu amonis to acute stressors.

The medial temporal lobe structure and function support positive affect

Positive affect (PA) is not only associated with individuals' psychological and physical health, but also their cognitive processes. However, whether medial temporal lobe (MTL) and its subfields' volume/functional connectivity can explain individual variability in PA remains understudied. We investigated the morphological (i.e., grey matter volume; GMV) and functional characteristics (i.e., resting-state functional connectivity; rsFC) of PA with a combination of univariate and multivariate pattern analyses (MVPA) using a large sample of participants (n = 321). We simultaneously collected the T1-weighted (n = 321), high-resolution MTL T2-weighted, and resting-state functional imaging data (n = 209). The MTL and its subfields' volumes, including the CA1, CA2+3, DG, and subiculum (SUB), perirhinal cortex (PRC), and parahippocampus (PHC), were extracted using an automatic segmentation of hippocampal subfields (ASHS) software. The morphological results revealed that GMVs in the prefrontal-occipital and limbic (i.e., hippocampus, amygdala, and PHC) systems were associated with variability in PA at the whole-brain level using MVPA but not univariate analysis. Linear regression results further revealed a positive association between the MTL subfields’ GMV, especially for the right PRC, and PA after controlling for several covariates. PRC-seed-based rsFC analyses further revealed that its couplings with the fronto-parietal-occipital system predicted PA in both univariate and MVPA. These findings provide novel insights into the neuroanatomical and functional substrates underlying human PA trait. Findings also suggest critical contributions of the MTL and its subfield of the perirhinal cortex, but not hippocampal subfields, as well as its functional coupling with the fronto-parietal control-system on the formation of PA.