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Research suggests structural and connectivity abnormalities in patients with treatment-resistant schizophrenia (TRS) compared to first-line responders and healthy-controls. However, measures of these abnormalities are often influenced by external factors like nicotine and antipsychotics, limiting their clinical utility. Intrinsic-cortical-curvature (ICC) presents a millimetre-scale measure of brain gyrification, highly sensitive to schizophrenia differences, and associated with TRS-like traits in early stages of the disorder. Despite this evidence, ICC in TRS remains unexplored. This study investigates ICC as a marker for treatment resistance in TRS, alongside structural indices for comparison. We assessed ICC in anterior cingulate, dorsolateral prefrontal, temporal, and parietal cortices of 38 first-line responders, 30 clozapine-resistant TRS, 37 clozapine-responsive TRS, and 52 healthy-controls. For comparative purposes, Fold and Curvature indices were also analyzed. Adjusting for age, sex, nicotine-use, and chlorpromazine equivalence, principal findings indicate ICC elevations in the left hemisphere dorsolateral prefrontal (p < 0.001, η2partial = 0.142) and temporal cortices (LH p = 0.007, η2partial = 0.060; RH p = 0.011, η2partial = 0.076) of both TRS groups, and left anterior cingulate cortex of clozapine-resistant TRS (p = 0.026, η2partial = 0.065), compared to healthy-controls. Elevations that correlated with reduced cognition (p = 0.001) and negative symptomology (p < 0.034) in clozapine-resistant TRS. Fold and Curvature indices only detected group differences in the right parietal cortex, showing interactions with age, sex, and nicotine use. ICC showed interactions with age. ICC elevations were found among patients with TRS, and correlated with symptom severity. ICCs relative independence from sex, nicotine-use, and antipsychotics, may support ICC's potential as a viable marker for TRS, though age interactions should be considered. [ABSTRACT FROM AUTHOR]

Background: The Huntington's Disease Integrated Staging System (HD-ISS) defined disease onset using volumetric cut-offs for caudate and putamen derived from FreeSurfer 6 (FS6). The impact of the latest software update (FS7) on volumes remains unknown. The Huntington's Disease Young Adult Study (HD-YAS) is appropriately positioned to explore differences in FS bias when detecting early atrophy. Objective: Explore the relationships and differences between raw caudate and putamen volumes, calculated total intracranial volumes (cTICV), and adjusted caudate and putamen volumes, derived from FS6 and FS7, in HD-YAS. Methods: Images from 123 participants were segmented and quality controlled. Relationships and differences between volumes were explored using intraclass correlation (ICC) and Bland-Altman analysis. Results: Across the whole cohort, ICC for raw caudate and putamen was 0.99, cTICV 0.93, adjusted caudate 0.87, and adjusted putamen 0.86 (all p < 0.0005). Compared to FS6, FS7 calculated: i) larger raw caudate (+0.8%, p < 0.00005) and putamen (+1.9%, p < 0.00005), with greater difference for larger volumes; and ii) smaller cTICV (–5.1%, p < 0.00005), with greater difference for smaller volumes. The systematic and proportional difference in cTICV was greater than raw volumes. When raw volumes were adjusted for cTICV, these effects compounded (adjusted caudate +7.0%, p < 0.00005; adjusted putamen +8.2%, p < 0.00005), with greater difference for larger volumes. Conclusions: As new software is released, it is critical that biases are explored since differences have the potential to significantly alter the findings of HD trials. Until conversion factors are defined, the HD-ISS must be applied using FS6. This should be incorporated into the HD-ISS online calculator. [ABSTRACT FROM AUTHOR]

At present, current diagnostic criteria and systems neglect affective symptom expression in catatonia. This potentially serious omission could explain why putative contributions of limbic system structures, such as amygdala, hippocampus or hypothalamus, to catatonia in schizophrenia spectrum disorders (SSD) have been scarcely investigated so far. To determine whether topographical alterations of the amygdala, hippocampus and hypothalamus contribute to catatonia in SSD patients, we conducted structural magnetic resonance imaging (MRI) of SSD patients with (SSD-Cat, n = 30) and without (SSD-nonCat, n = 28) catatonia as defined by a Northoff Catatonia Rating Scale (NCRS) total score of ≥3 and =0, respectively, in comparison with healthy controls (n = 20). FreeSurfer v7.2 was used for automated segmentation of the amygdala and its 9 nuclei, hippocampus and its 21 subfields and hypothalamus and its associated 5 subunits. SSD-Cat had significantly smaller anterior inferior hypothalamus, cortical nucleus of amygdala, and hippocampal fimbria volumes when compared to SSD-nonCat. SSD-Cat had significantly smaller amygdala, hippocampus and hypothalamus whole and subunit volumes when compared to healthy controls. In SSD-Cat according to DSM-IV-TR (n = 44), we identified positive correlations between Brief Psychiatric Rating Scale (BPRS) item #2 (reflecting anxiety) and respective amygdala nuclei as well as negative correlation between NCRS behavioral score and hippocampus subiculum head. The lower volumes of respective limbic structures involved in affect regulation may point towards central affective pathomechanisms in catatonia. [ABSTRACT FROM AUTHOR]

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.

Introduction: Accurate segmentation of brain tissue in magnetic resonance imaging (MRI) is an important step in the analysis of brain images. There are automated methods used to segmentation the brain and minimize the disadvantages of manual segmentation, including time consuming and misinterpretations. These procedures usually involve a combination of skull removal, bias field correction, and segmentation. Therefore, segmented tissue quality assessment segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is required for the analysis of neuroimages. Material and Methods: This paper presents the performance evaluation of three automatic methods brain segmentation, fluid and white matter suppression [FSL, Freesurfer (FreeSurfer is an open source package for the analysis and visualization of structural, functional, and diffusion neuroimaging data from cross-sectional and longitudinal studies) and SPM12 (Statistical Parametric Mapping)]. Segmentation with SPM12 was performed on three tissue probability maps: i) threshold 0.5, ii) threshold 0.7 and iii) threshold 0.9. In order to compare and evaluate the automatic methods, the reference standard method, i.e., manual segmentation, was performed by three radiologists. Results: Comparison of GM, WM and CSF segmentation in MR images was performed using similarities between manual and automatic segmentation. The similarity between the segmented tissues was calculated using diagnostic criteria. Conclusion: Several studies have examined the classification of GM, WM, and CSF using software packages. In these studies, different results have been obtained depending on the type of method and images used and the type of segmented tissues. In this study, the evaluation of the segmentation of these packages with reference standard method is performed. The results can help users in selecting an appropriate segmentation tool for neuroimages analysis.

Spurred by availability of automatic segmentation software, in vivo MRI investigations of human hippocampal subfield volumes have proliferated in the recent years. However, a majority of these studies apply automatic segmentation to MRI scans with approximately 1 × 1 × 1 mm3 resolution, a resolution at which the internal structure of the hippocampus can rarely be visualized. Many of these studies have reported contradictory and often neurobiologically surprising results pertaining to the involvement of hippocampal subfields in normal brain function, aging, and disease. In this commentary, we first outline our concerns regarding the utility and validity of subfield segmentation on 1 × 1 × 1 mm3 MRI for volumetric studies, regardless of how images are segmented (i.e., manually or automatically). This image resolution is generally insufficient for visualizing the internal structure of the hippocampus, particularly the stratum radiatum lacunosum moleculare, which is crucial for valid and reliable subfield segmentation. Second, we discuss the fact that automatic methods that are employed most frequently to obtain hippocampal subfield volumes from 1 × 1 × 1 mm3 MRI have not been validated against manual segmentation on such images. For these reasons, we caution against using volumetric measurements of hippocampal subfields obtained from 1 × 1 × 1 mm3 images.

We have studied the effects of manual quality control of brain Magnetic Resonance Imaging (MRI) images processed with Freesurfer. T1 images of first episode psychosis patients (N = 60) and healthy controls (N = 41) were inspected for gray matter boundary errors. The errors were fixed, and the effects of error correction on brain volume, thickness, and surface area were measured.It is commonplace to apply quality control to Freesurfer MRI recordings to ensure that the edges of gray and white matter are detected properly, as incorrect edge detection leads to changes in variables such as volume, cortical thickness, and cortical surface area. We find that while Freesurfer v7.1.1. does regularly make mistakes in identifying the edges of cortical gray matter, correcting these errors yields limited changes in the commonly measured variables listed above. We further find that the software makes fewer gray matter boundary errors when processing female brains.The results suggest that manually correcting gray matter boundary errors may not be worthwhile due to its small effect on the measurements, with potential exceptions for studies that focus on the areas that are more commonly affected by errors: the areas around the cerebellar tentorium, paracentral lobule, and the optic nerves, specifically the horizontal segment of the middle cerebral artery.

Introduction: Accurate segmentation of brain tissue in magnetic resonance imaging (MRI) is an important step in the analysis of brain images. There are automated methods used to segmentation the brain and minimize the disadvantages of manual segmentation, including time consuming and misinterpretations. These procedures usually involve a combination of skull removal, bias field correction, and segmentation. Therefore, segmented tissue quality assessment segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is required for the analysis of neuroimages. Material and Methods: This paper presents the performance evaluation of three automatic methods brain segmentation, fluid and white matter suppression [FSL, Freesurfer (FreeSurfer is an open source package for the analysis and visualization of structural, functional, and diffusion neuroimaging data from cross-sectional and longitudinal studies) and SPM12 (Statistical Parametric Mapping)]. Segmentation with SPM12 was performed on three tissue probability maps: i) threshold 0.5, ii) threshold 0.7 and iii) threshold 0.9. In order to compare and evaluate the automatic methods, the reference standard method, i.e., manual segmentation, was performed by three radiologists. Results: Comparison of GM, WM and CSF segmentation in MR images was performed using similarities between manual and automatic segmentation. The similarity between the segmented tissues was calculated using diagnostic criteria. Conclusion: Several studies have examined the classification of GM, WM, and CSF using software packages. In these studies, different results have been obtained depending on the type of method and images used and the type of segmented tissues. In this study, the evaluation of the segmentation of these packages with reference standard method is performed. The results can help users in selecting an appropriate segmentation tool for neuroimages analysis. [ABSTRACT FROM AUTHOR]

Objective: Kallmann's syndrome (KS) is characterized by hypogonadotropic hypogonadism and olfactory disorders. The complementary exams for evaluating of patients with hypogonadotrophic hypogonadism are important for the diagnosis and management of these patients. Patients: We performed a well‐established olfactory Sniffin' Stick test (SST) on 17 adult patients with KS and brain magnetic resonance imaging (MRI) to evaluate olfactory structures and further analysis by Freesurfer, a software for segmentation and volumetric evaluation of brain structures. We compared the Freesurfer results with 34 healthy patients matched for age and sex and performed correlations between the data studied. Results: More than half of the patients with KS reported preserved smell but had olfactory disorders in the SST. In the MRI, 16 patients showed changes in the olfactory groove, the olfactory bulb‐tract complex was altered in all of them and 52% had symmetrical structural changes. Interestingly, the pituitary gland was normal in only 29%. Regarding correlations, symmetrical changes in the olfactory structures were related to anosmia in 100%, while asymmetric changes induced anosmia in only 50% (p =.0294). In Freesurfer's assessment, patients with KS, compared to controls, had lower brainstem volume. In those with aplastic anterior olfactory sulcus, the brainstem volume was lower than in hypoplasia (p =.0333). Conclusions: Olfactory assessment and MRI proved to be important auxiliary tools for the diagnosis and management of patients with KS. New studies are needed to confirm the decrease in brainstem volume found by the Freesurfer software in patients with KS. Further studies are needed to confirm the decrease in brainstem volume found by the Freesurfer software in patients with KS. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI) is an important public health problem that has enormous consequences for patients, their families, the health care system, and the economy. MCI is highly likely to progress to dementia. Early premedical intervention for MCI could prevent or delay dementia's onset, which in turn would improve patients' quality of life. Thus, the goal of our research is to develop an MCI detection system using MRI-based measures. The dataset for the study consists of 62 healthy volunteers and 145 patients with MCI obtained from the «Memory Clinic» (Moscow, Russia). The morphometric data were extracted from T1-weighted structural MR images. We propose a machine learning method, which achieved an accuracy of 73%, and extract key features in brain structures that can distinguish the healthy control (HC) group from the MCI group. This study confirms the previously identified changes in the structural regions of the brain in MCI patients. Compared to the healthy control group, in the MCI group, volume increases in the Lateral Ventricle, Inferior Lateral Ventricle, and Cerebrospinal Fluid as well as volume reductions in the Amygdala, Hippocampus, and Accumbens have been found. [ABSTRACT FROM AUTHOR]

To investigate the association between the volume of amygdala subnuclei and violent behaviors in patients with schizophrenia (SCZ). In the present study, we recruited 40 SCZ patients with violent behaviors (VS), 26 SCZ patients without violent behaviors (NVS), and 28 matched healthy controls (HC) who completed T1-weighted magnetic resonance imaging. Both the total amygdala and amygdala subnuclei volumes were estimated with FreeSurfer. When comparing the SCZ patients with HC, SCZ patients had a smaller volume of the left basal nucleus (P < 0.05, uncorrected). Further, the VS patients had a smaller volume of the left amygdala central nucleus than the NVS group (P < 0.05, Bonferroni corrected). Our study suggests that a smaller volume of the left amygdala basal nucleus may be a biomarker for SCZ and that a smaller volume of the left central nucleus may be relevant to violence risk in patients with schizophrenia. [ABSTRACT FROM AUTHOR]

Background: Maternal smoking during pregnancy has been shown to associate with smaller frontal lobe and cerebellar volumes in brain magnetic resonance imaging (MRI) at term age in very preterm infants. The aim of this study was to examine the effect of maternal smoking during pregnancy on volumetric brain MRI findings at 13 years. We hypothesized that adverse effects of smoking during pregnancy on brain volumes are still seen during adolescence. Methods: Included adolescents were born very preterm (gestational age < 32 weeks and/or birth weight ≤ 1,500 g) between April 2004 and December 2006 at the Turku University Hospital, Finland. Information on maternal smoking status (yes or no) during pregnancy was collected from medical records and maternal questionnaires before discharge. Adolescents underwent volumetric brain MRI at 13 years of age. Image post-processing was performed with FreeSurfer. Regional volumes, cortical thickness, surface area, and curvature were computed from 33 cortical regions of interest (ROIs). Additionally, volumes were calculated for 18 subcortical regions, as well as for white matter, gray matter, and intracranial volume. We normalized quantified absolute volumes for head size by dividing volumes with corresponding intracranial volumes. false discovery rate (FDR) correction for multiple comparisons across regions was used. Results: A total of 9/44 (21%) adolescents had been exposed to maternal smoking during pregnancy. No statistically significant differences in absolute volumes were observed between the groups (p > 0.05). Regarding volumes proportional to intracranial volume, the adolescents in the exposed group exhibited smaller gray matter volumes in the inferotemporal (FDR corrected p = 0.022) and parahippocampal (p = 0.018) regions compared to the unexposed group. The surface area in the exposed group was also smaller in the parahippocampal (p = 0.046) and postcentral (p = 0.046) regions compared to the unexposed group. No statistically significant differences after correction for multiple comparisons were found for either curvature or cortical thickness between the groups. Conclusion: Maternal smoking exposure during pregnancy may have longterm effects on brain volumes up to 13 years in adolescents born very preterm. Our findings emphasize the importance of smoking-free pregnancy. [ABSTRACT FROM AUTHOR]

BackgroundMaternal smoking during pregnancy has been shown to associate with smaller frontal lobe and cerebellar volumes in brain magnetic resonance imaging (MRI) at term age in very preterm infants. The aim of this study was to examine the effect of maternal smoking during pregnancy on volumetric brain MRI findings at 13 years. We hypothesized that adverse effects of smoking during pregnancy on brain volumes are still seen during adolescence.MethodsIncluded adolescents were born very preterm (gestational age < 32 weeks and/or birth weight ≤ 1,500 g) between April 2004 and December 2006 at the Turku University Hospital, Finland. Information on maternal smoking status (yes or no) during pregnancy was collected from medical records and maternal questionnaires before discharge. Adolescents underwent volumetric brain MRI at 13 years of age. Image post-processing was performed with FreeSurfer. Regional volumes, cortical thickness, surface area, and curvature were computed from 33 cortical regions of interest (ROIs). Additionally, volumes were calculated for 18 subcortical regions, as well as for white matter, gray matter, and intracranial volume. We normalized quantified absolute volumes for head size by dividing volumes with corresponding intracranial volumes. false discovery rate (FDR) correction for multiple comparisons across regions was used.ResultsA total of 9/44 (21%) adolescents had been exposed to maternal smoking during pregnancy. No statistically significant differences in absolute volumes were observed between the groups (p > 0.05). Regarding volumes proportional to intracranial volume, the adolescents in the exposed group exhibited smaller gray matter volumes in the inferotemporal (FDR corrected p = 0.022) and parahippocampal (p = 0.018) regions compared to the unexposed group. The surface area in the exposed group was also smaller in the parahippocampal (p = 0.046) and postcentral (p = 0.046) regions compared to the unexposed group. No statistically significant differences after correction for multiple comparisons were found for either curvature or cortical thickness between the groups.ConclusionMaternal smoking exposure during pregnancy may have long-term effects on brain volumes up to 13 years in adolescents born very preterm. Our findings emphasize the importance of smoking-free pregnancy.

ObjectiveBrain imaging studies of structural abnormalities in OCD have yielded inconsistent results, partly because of limited statistical power, clinical heterogeneity, and methodological differences. The authors conducted meta- and mega-analyses comprising the largest study of cortical morphometry in OCD ever undertaken.MethodT1-weighted MRI scans of 1,905 OCD patients and 1,760 healthy controls from 27 sites worldwide were processed locally using FreeSurfer to assess cortical thickness and surface area. Effect sizes for differences between patients and controls, and associations with clinical characteristics, were calculated using linear regression models controlling for age, sex, site, and intracranial volume.ResultsIn adult OCD patients versus controls, we found a significantly lower surface area for the transverse temporal cortex and a thinner inferior parietal cortex. Medicated adult OCD patients also showed thinner cortices throughout the brain. In pediatric OCD patients compared with controls, we found significantly thinner inferior and superior parietal cortices, but none of the regions analyzed showed significant differences in surface area. However, medicated pediatric OCD patients had lower surface area in frontal regions. Cohen's d effect sizes varied from -0.10 to -0.33.ConclusionsThe parietal cortex was consistently implicated in both adults and children with OCD. More widespread cortical thickness abnormalities were found in medicated adult OCD patients, and more pronounced surface area deficits (mainly in frontal regions) were found in medicated pediatric OCD patients. These cortical measures represent distinct morphological features and may be differentially affected during different stages of development and illness, and possibly moderated by disease profile and medication.

BackgroundOur understanding of the complex relationship between schizophrenia symptomatology and etiological factors can be improved by studying brain-based correlates of schizophrenia. Research showed that impairments in value processing and executive functioning, which have been associated with prefrontal brain areas [particularly the medial orbitofrontal cortex (MOFC)], are linked to negative symptoms. Here we tested the hypothesis that MOFC thickness is associated with negative symptom severity.MethodsThis study included 1985 individuals with schizophrenia from 17 research groups around the world contributing to the ENIGMA Schizophrenia Working Group. Cortical thickness values were obtained from T1-weighted structural brain scans using FreeSurfer. A meta-analysis across sites was conducted over effect sizes from a model predicting cortical thickness by negative symptom score (harmonized Scale for the Assessment of Negative Symptoms or Positive and Negative Syndrome Scale scores).ResultsMeta-analytical results showed that left, but not right, MOFC thickness was significantly associated with negative symptom severity (β std = -0.075; p = 0.019) after accounting for age, gender, and site. This effect remained significant (p = 0.036) in a model including overall illness severity. Covarying for duration of illness, age of onset, antipsychotic medication or handedness weakened the association of negative symptoms with left MOFC thickness. As part of a secondary analysis including 10 other prefrontal regions further associations in the left lateral orbitofrontal gyrus and pars opercularis emerged.ConclusionsUsing an unusually large cohort and a meta-analytical approach, our findings point towards a link between prefrontal thinning and negative symptom severity in schizophrenia. This finding provides further insight into the relationship between structural brain abnormalities and negative symptoms in schizophrenia.

We analysed a dataset comprising 118 subjects who were scanned three times (at baseline, 1‐year follow‐up, and 7‐year follow‐up) using structural magnetic resonance imaging (MRI) over the course of 7 years. We aimed to examine whether it is possible to identify individual subjects based on a restricted number of neuroanatomical features measured 7 years previously. We used FreeSurfer to compute 15 standard brain measures (total intracranial volume [ICV], total cortical thickness [CT], total cortical surface area [CA], cortical grey matter [CoGM], cerebral white matter [CeWM], cerebellar cortex [CBGM], cerebellar white matter [CBWM], subcortical volumes [thalamus, putamen, pallidum, caudatus, hippocampus, amygdala and accumbens] and brain stem volume). We used linear discriminant analysis (LDA), random forest machine learning (RF) and a newly developed rule‐based identification approach (RBIA) for the identification process. Using RBIA, different sets of neuroanatomical features (ranging from 2 to 14) obtained at baseline were combined by if–then rules and compared to the same set of neuroanatomical features derived from the 7‐year follow‐up measurement. We achieved excellent identification results with LDA, while the identification results for RF were very good but not perfect. The RBIA produced the best results, achieving perfect participant identification for some four‐feature sets. The identification results improved substantially when using larger feature sets, with 14 neuroanatomical features providing perfect identification. Thus, this study shows again that the human brain is highly individual in terms of neuroanatomical features. These results are discussed in the context of the current literature on brain plasticity and the scientific attempts to develop brain‐fingerprinting techniques. [ABSTRACT FROM AUTHOR]

ObjectiveBased on the role of the superior temporal gyrus (STG) in auditory processing, language comprehension and self-monitoring, this study aimed to investigate the relationship between STG cortical thickness and positive symptom severity in schizophrenia.MethodThis prospective meta-analysis includes data from 1987 individuals with schizophrenia collected at seventeen centres around the world that contribute to the ENIGMA Schizophrenia Working Group. STG thickness measures were extracted from T1-weighted brain scans using FreeSurfer. The study performed a meta-analysis of effect sizes across sites generated by a model predicting left or right STG thickness with a positive symptom severity score (harmonized SAPS or PANSS-positive scores), while controlling for age, sex and site. Secondary models investigated relationships between antipsychotic medication, duration of illness, overall illness severity, handedness and STG thickness.ResultsPositive symptom severity was negatively related to STG thickness in both hemispheres (left: βstd = -0.052; P = 0.021; right: βstd = -0.073; P = 0.001) when statistically controlling for age, sex and site. This effect remained stable in models including duration of illness, antipsychotic medication or handedness.ConclusionOur findings further underline the important role of the STG in hallmark symptoms in schizophrenia. These findings can assist in advancing insight into symptom-relevant pathophysiological mechanisms in schizophrenia.

Objective: Pediatric obsessive-compulsive disorder (OCD) and clinically relevant obsessive-compulsive symptoms in the general population are associated with increased thalamic volume. It is unknown whether this enlargement is explained by specific thalamic subregions. The relation between obsessive-compulsive symptoms and volume of thalamic subregions was investigated in a population-based sample of children.Method: Obsessive-compulsive symptoms were measured in children (9-12 years of age) from the Generation R Study using the Short Obsessive-Compulsive Disorder Screener (SOCS). Thalamic nuclei volumes were extracted from structural 3T magnetic resonance imaging scans using the ThalamicNuclei pipeline and regrouped into anterior, ventral, intralaminar/medial, lateral, and pulvinar subregions. Volumes were compared between children with symptoms above clinical cutoff (probable OCD cases, SOCS ≥ 6, n = 156) and matched children without symptoms (n = 156). Linear regression models were fitted to investigate the association between continuous SOCS score and subregional volume in the whole sample (N = 2500).Results: Children with probable OCD had larger ventral nuclei compared with children without symptoms (d = 0.25, p = .025, false discovery rate adjusted p = .126). SOCS score showed a negative association with pulvinar volume when accounting for overall thalamic volume (β = -0.057, p = .009, false discovery rate adjusted p = .09). However, these associations did not survive multiple testing correction.Conclusion: The results suggest that individual nuclei groups contribute in varying degrees to overall thalamic volume in children with probable OCD, although this did not survive multiple comparisons correction. Understanding the role of thalamic nuclei and their associated circuits in pediatric OCD could lead toward treatment strategies targeting these circuits. [ABSTRACT FROM AUTHOR]

A wide spectrum of neurocognitive deficits characterises HIV infection in adults. HIV infection is additionally associated with morphological brain abnormalities affecting neural substrates that subserve neurocognitive function. Early life stress (ELS) also has a direct influence on brain morphology. However, the combined impact of ELS and HIV on brain structure and neurocognitive function has not been examined in an all-female sample with advanced HIV disease. The present study examined the effects of HIV and childhood trauma on brain morphometry and neurocognitive function. Structural data were acquired using a 3T Magnetom MRI scanner, and a battery of neurocognitive tests was administered to 124 women: HIV-positive with ELS (n = 32), HIV-positive without ELS (n = 30), HIV-negative with ELS (n = 31) and HIV-negative without ELS (n = 31). Results revealed significant group volumetric differences for right anterior cingulate cortex (ACC), bilateral hippocampi, corpus callosum, left and right caudate and left and right putamen. Mean regional volumes were lowest in HIV-positive women with ELS compared to all other groups. Although causality cannot be inferred, findings also suggest that alterations in the left frontal lobe, right ACC, left hippocampus, corpus callosum, left and right amygdala and left caudate may be associated with poorer neurocognitive performance in the domains of processing speed, attention/working memory, abstraction/executive functions, motor skills, learning and language/fluency with these effects more pronounced in women living with both HIV and childhood trauma. This study highlights the potential contributory role of childhood trauma to brain alterations and neurocognitive decline in HIV-infected individuals.

The relative roles of brainstem, thalamus and striatum in parkinsonism in schizophrenia spectrum disorder (SSD) patients are largely unknown. To determine whether topographical alterations of the brainstem, thalamus and striatum contribute to parkinsonism in SSD patients, we conducted structural magnetic resonance imaging (MRI) of SSD patients with (SSD-P, n = 35) and without (SSD-nonP, n = 64) parkinsonism, as defined by a Simpson and Angus Scale (SAS) total score of ≥ 4 and < 4, respectively, in comparison with healthy controls (n = 20). FreeSurfer v6.0 was used for segmentation of four brainstem regions (medulla oblongata, pons, superior cerebellar peduncle and midbrain), caudate nucleus, putamen and thalamus. Patients with parkinsonism had significantly smaller medulla oblongata (p = 0.01, false discovery rate (FDR)-corrected) and putamen (p = 0.02, FDR-corrected) volumes when compared to patients without parkinsonism. Across the entire patient sample (n = 99), significant negative correlations were identified between (a) medulla oblongata volumes and both SAS total (p = 0.034) and glabella-salivation (p = 0.007) scores, and (b) thalamic volumes and both SAS total (p = 0.033) and glabella-salivation (p = 0.007) scores. These results indicate that brainstem and thalamic structures as well as basal ganglia-based motor circuits play a crucial role in the pathogenesis of parkinsonism in SSD. [ABSTRACT FROM AUTHOR]

Background: The cerebellar ataxia, neuropathy, and vestibular areflexia syndrome was initially described in the early 1990s as a late‐onset slowly progressive condition. Its underlying genetic cause was recently mapped to the RFC1 gene, and additional reports have expanded on the phenotypic manifestations related to RFC1, although little is known about the pattern and extent of structural brain abnormalities in this condition. Objective: The aim is to characterize the structural signature of brain damage in RFC1‐related disorder, correlating the findings with clinical symptoms and normal brain RFC1 expression. Methods: We recruited 22 individuals with molecular confirmation of RFC1 expansions and submitted them to high‐resolution 3T magnetic resonance imaging scans. We performed multimodal analyses to assess separately cerebral and cerebellar abnormalities within gray and white matter (WM). The results were compared with a group of 22 age‐ and sex‐matched controls. Results: The mean age and disease duration of patients were 62.8 and 10.9 years, respectively. Ataxia, sensory neuronopathy, and vestibular areflexia were the most frequent manifestations, but parkinsonism and pyramidal signs were also noticed. We found that RFC1‐related disorder is characterized by widespread and relatively symmetric cerebellar and basal ganglia atrophy. There is brainstem volumetric reduction along all its segments. Cerebral WM is also involved—mostly the corpus callosum and deep tracts, but cerebral cortical damage is rather restricted. Conclusion: This study adds new relevant insights into the pathophysiological mechanisms of RFC1‐related disorder. It should no longer be considered a purely cerebellar and sensory pathway disorder. Basal ganglia and deep cerebral WM are additional targets of damage. © 2021 International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

Multisite magnetic resonance imaging (MRI) is increasingly used in clinical research and development. Measurement biases—caused by site differences in scanner/image‐acquisition protocols—negatively influence the reliability and reproducibility of image‐analysis methods. Harmonization can reduce bias and improve the reproducibility of multisite datasets. Herein, a traveling‐subject (TS) dataset including 56 T1‐weighted MRI scans of 20 healthy participants in three different MRI procedures—20, 19, and 17 subjects in Procedures 1, 2, and 3, respectively—was considered to compare the reproducibility of TS‐GLM, ComBat, and TS‐ComBat harmonization methods. The minimum participant count required for harmonization was determined, and the Cohen's d between different MRI procedures was evaluated as a measurement‐bias indicator. The measurement‐bias reduction realized with different methods was evaluated by comparing test–retest scans for 20 healthy participants. Moreover, the minimum subject count for harmonization was determined by comparing test–retest datasets. The results revealed that TS‐GLM and TS‐ComBat reduced measurement bias by up to 85 and 81.3%, respectively. Meanwhile, ComBat showed a reduction of only 59.0%. At least 6 TSs were required to harmonize data obtained from different MRI scanners, complying with the imaging protocol predetermined for multisite investigations and operated with similar scan parameters. The results indicate that TS‐based harmonization outperforms ComBat for measurement‐bias reduction and is optimal for MRI data in well‐prepared multisite investigations. One drawback is the small sample size used, potentially limiting the applicability of ComBat. Investigation on the number of subjects needed for a large‐scale study is an interesting future problem. [ABSTRACT FROM AUTHOR]

The accurate segmentation of in vivo magnetic resonance imaging (MRI) data is a crucial prerequisite for the reliable assessment of disease progression, patient stratification or the establishment of putative imaging biomarkers. This is especially important for the hippocampal formation, a brain area involved in memory formation and often affected by neurodegenerative or psychiatric diseases. FreeSurfer, a widely used automated segmentation software, offers hippocampal subfield delineation with multiple input options. While a single T1-weighted (T1) sequence is regularly used by most studies, it is also possible and advised to use a high-resolution T2-weighted (T2H) sequence or multispectral information. In this investigation it was determined whether there are differences in volume estimations depending on the input images and which combination of these deliver the most reliable results in each hippocampal subfield. 41 healthy participants (age = 25.2 years ± 4.2 SD) underwent two structural MRIs at three Tesla (time between scans: 23 days ± 11 SD) using three different structural MRI sequences, to test five different input configurations (T1, T2, T2H, T1 and T2, and T1 and T2H). We compared the different processing pipelines in a cross-sectional manner and assessed reliability using test-retest variability (%TRV) and the dice coefficient. Our analyses showed pronounced significant differences and large effect sizes between the processing pipelines in several subfields, such as the molecular layer (head), CA1 (head), hippocampal fissure, CA3 (head and body), fimbria and CA4 (head). The longitudinal analysis revealed that T1 and multispectral analysis (T1 and T2H) showed overall higher reliability across all subfields than T2H alone. However, the specific subfields had a substantial influence on the performance of segmentation results, regardless of the processing pipeline. Although T1 showed good test-retest metrics, results must be interpreted with caution, as a standard T1 sequence relies heavily on prior information of the atlas and does not take the actual fine structures of the hippocampus into account. For the most accurate segmentation, we advise the use of multispectral information byusing a combination of T1 and high-resolution T2-weighted sequences or a T2 high-resolution sequence alone. [ABSTRACT FROM AUTHOR]

The accurate segmentation of in vivo magnetic resonance imaging (MRI) data is a crucial prerequisite for the reliable assessment of disease progression, patient stratification or the establishment of putative imaging biomarkers. This is especially important for the hippocampal formation, a brain area involved in memory formation and often affected by neurodegenerative or psychiatric diseases. FreeSurfer, a widely used automated segmentation software, offers hippocampal subfield delineation with multiple input options. While a single T1-weighted (T1) sequence is regularly used by most studies, it is also possible and advised to use a high-resolution T2-weighted (T2H) sequence or multispectral information. In this investigation it was determined whether there are differences in volume estimations depending on the input images and which combination of these deliver the most reliable results in each hippocampal subfield. 41 healthy participants (age = 25.2 years ± 4.2 SD) underwent two structural MRIs at three Tesla (time between scans: 23 days ± 11 SD) using three different structural MRI sequences, to test five different input configurations (T1, T2, T2H, T1 and T2, and T1 and T2H). We compared the different processing pipelines in a cross-sectional manner and assessed reliability using test-retest variability (%TRV) and the dice coefficient. Our analyses showed pronounced significant differences and large effect sizes between the processing pipelines in several subfields, such as the molecular layer (head), CA1 (head), hippocampal fissure, CA3 (head and body), fimbria and CA4 (head). The longitudinal analysis revealed that T1 and multispectral analysis (T1 and T2H) showed overall higher reliability across all subfields than T2H alone. However, the specific subfields had a substantial influence on the performance of segmentation results, regardless of the processing pipeline. Although T1 showed good test-retest metrics, results must be interpreted with caution, as a standard T1 sequence relies heavily on prior information of the atlas and does not take the actual fine structures of the hippocampus into account. For the most accurate segmentation, we advise the use of multispectral information by using a combination of T1 and high-resolution T2-weighted sequences or a T2 high-resolution sequence alone.

Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, we present an explainable DL framework to predict the age of a healthy cohort of subjects from ABIDE I database by using the morphological features extracted from their MRI scans. We embed the two local XAI methods SHAP and LIME to explain the outcomes of the DL models, determine the contribution of each brain morphological descriptor to the final predicted age of each subject and investigate the reliability of the two methods. Our findings indicate that the SHAP method can provide more reliable explanations for the morphological aging mechanisms and be exploited to identify personalized age-related imaging biomarker. [ABSTRACT FROM AUTHOR]

A method that can be used in the early stage of multiple sclerosis (MS) to predict the progression of brain volume loss (BVL) has not been fully established. To develop a method of predicting progressive BVL in patients with MS (pwMS), eighty-two consecutive Japanese pwMS—with either relapsing-remitting MS (86%) or secondary progressive MS (14%)—and 41 healthy controls were included in this longitudinal retrospective analysis over an observational period of approximately 3.5 years. Using a hierarchical cluster analysis with multivariate imaging data obtained by FreeSurfer analysis, we classified the pwMS into clusters. At baseline and follow-up, pwMS were cross-sectionally classified into three major clusters (Clusters 1, 2, and 3) in ascending order by disability and BVL. Among the patients included in Cluster 1 at baseline, approximately one-third of patients (12/52) transitioned into Cluster 2 at follow-up. The volumes of the corpus callosum, the thalamus, and the whole brain excluding the ventricles were significantly decreased in the transition group compared with the nontransition group and were found to be the most important predictors of transition. Decreased volumes of the corpus callosum and thalamus in the relatively early stage of MS may predict the development of BVL. • Patients with MS were classified into three clusters at baseline and follow-up. • Each cluster was characterized by the degree of brain volume loss (BVL). • One-third of patients in the mild BVL group transitioned into the moderate BVL group. • The volumes of the corpus callosum and the thalamus predicted transition. [ABSTRACT FROM AUTHOR]

A wide spectrum of neurocognitive deficits characterises HIV infection in adults. HIV infection is additionally associated with morphological brain abnormalities affecting neural substrates that subserve neurocognitive function. Early life stress (ELS) also has a direct influence on brain morphology. However, the combined impact of ELS and HIV on brain structure and neurocognitive function has not been examined in an all-female sample with advanced HIV disease. The present study examined the effects of HIV and childhood trauma on brain morphometry and neurocognitive function. Structural data were acquired using a 3T Magnetom MRI scanner, and a battery of neurocognitive tests was administered to 124 women: HIV-positive with ELS (n = 32), HIV-positive without ELS (n = 30), HIV-negative with ELS (n = 31) and HIV-negative without ELS (n = 31). Results revealed significant group volumetric differences for right anterior cingulate cortex (ACC), bilateral hippocampi, corpus callosum, left and right caudate and left and right putamen. Mean regional volumes were lowest in HIV-positive women with ELS compared to all other groups. Although causality cannot be inferred, findings also suggest that alterations in the left frontal lobe, right ACC, left hippocampus, corpus callosum, left and right amygdala and left caudate may be associated with poorer neurocognitive performance in the domains of processing speed, attention/working memory, abstraction/executive functions, motor skills, learning and language/fluency with these effects more pronounced in women living with both HIV and childhood trauma. This study highlights the potential contributory role of childhood trauma to brain alterations and neurocognitive decline in HIV-infected individuals.

Objective: Obsessive-compulsive (OC) symptoms are common in the general population, but it is unclear whether subclinical OC symptoms and obsessive-compulsive disorder (OCD) are part of a neuroanatomical continuum. The goal of this study was to investigate the relation between OC symptoms and subcortical and cortical morphology in a population-based sample of children.Method: The study included 2,551 participants, aged 9-12 years, from the population-based Generation R Study. OC symptoms were measured using the 7-item caregiver-rated Short Obsessive-Compulsive Disorder Screener (SOCS). Structural (3T) magnetic resonance imaging scans were processed using FreeSurfer to study the thalamus and other subcortical volumes, intracranial volume, vertexwise cortical thickness, and surface area. We used linear regression models to investigate the association between OC symptoms and brain morphology. Emulating case-control studies from the literature, we compared children scoring above the clinical cutoff of the SOCS (probable OCD cases, n = 164) with matched children without symptoms.Results: Children with probable OCD had larger thalami compared with the control group (d 0.16, p = .044). Vertexwise analysis showed a positive association between OC symptoms and thickness of the right inferior parietal cortex, which disappeared after adjusting for total behavioral problems. SOCS scores correlated negatively with intracranial volume (B = -2444, p = .038).Conclusion: Children with probable OCD showed thalamus alterations similar to those previously reported in unmedicated children with OCD. OC symptoms showed a stronger association with total intracranial volume than regional brain measures. Longitudinal studies are needed to further elucidate similarities and distinctions between neural correlates of subclinical and clinical OC symptoms. [ABSTRACT FROM AUTHOR]

Spurred by availability of automatic segmentation software, in vivo MRI investigations of human hippocampal subfield volumes have proliferated in the recent years. However, a majority of these studies apply automatic segmentation to MRI scans with approximately 1 × 1 × 1 mm3 resolution, a resolution at which the internal structure of the hippocampus can rarely be visualized. Many of these studies have reported contradictory and often neurobiologically surprising results pertaining to the involvement of hippocampal subfields in normal brain function, aging, and disease. In this commentary, we first outline our concerns regarding the utility and validity of subfield segmentation on 1 × 1 × 1 mm3 MRI for volumetric studies, regardless of how images are segmented (i.e., manually or automatically). This image resolution is generally insufficient for visualizing the internal structure of the hippocampus, particularly the stratum radiatum lacunosum moleculare, which is crucial for valid and reliable subfield segmentation. Second, we discuss the fact that automatic methods that are employed most frequently to obtain hippocampal subfield volumes from 1 × 1 × 1 mm3 MRI have not been validated against manual segmentation on such images. For these reasons, we caution against using volumetric measurements of hippocampal subfields obtained from 1 × 1 × 1 mm3 images. [ABSTRACT FROM AUTHOR]

Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results.

Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.

Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results. [ABSTRACT FROM AUTHOR]

Background: Manual region‐of‐interest analysis of putaminal and middle cerebellar peduncle diffusivity distinguishes patients with multiple system atrophy (MSA) and Parkinson's disease (PD) with high diagnostic accuracy. However, a recent meta‐analysis found substantial between‐study heterogeneity of diagnostic accuracy due to the lack of harmonized imaging protocols and standardized analyses pipelines. Objective: Evaluation of diagnostic accuracy of observer‐independent analysis of microstructural integrity as measured by diffusion‐tensor imaging in patients with MSA and PD. Methods: A total of 29 patients with MSA and 19 patients with PD (matched for age, gender, and disease duration) with 3 years of follow‐up were investigated with diffusion‐tensor imaging and T1‐weighted magnetic resonance imaging. Automated localization of relevant brain regions was obtained, and mean diffusivity and fractional anisotropy values were averaged within the regions of interest. The classification was performed using a C5.0 hierachical decision tree algorithm. Results: Mean diffusivity of the middle cerebellar peduncle and cerebellar gray and white matter compartment as well as the putamen were significantly increased in patients with MSA and showed superior effect sizes compared to the volumetric analysis of these regions. A classifier model identified mean diffusivity of the middle cerebellar peduncle and putamen as the most predictive parameters. Cross‐validation of the classification model yields a Cohen's κ and overall diagnostic accuracy of 0.823 and 0.914, respectively. Conclusion: Analysis of microstructural integrity within the middle cerebellar peduncle and putamen yielded a superior effect size compared to the volumetric measures, resulting in excellent diagnostic accuracy to discriminate patients with MSA from PD in the early to moderate disease stages. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

Background: Regional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases (CVD) can be estimated using specialized neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures. Purpose: The main goal of this study was to examine a correction procedure developed for enhancing FreeSurfer's (FS's) cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and CVD, with varying degrees of neurovascular lesions and brain atrophy. Methods: In 155 CVD participants enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FS outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures. Results: Corrected procedures increased "Acceptable" QC ratings from 18 to 76% for the cortical ribbon and from 38 to 92% for tissue segmentation. Corrected procedures reduced "Fail" ratings from 11 to 0% for the cortical ribbon and 62 to 8% for tissue segmentation. FS-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8 mL vs. 15.9 mL, p < 0.001). The unmodified procedure yielded no significant associations with global cognitive status, whereas the corrected procedure yielded positive associations between MoCA total score and clusters of cortical thickness in the left superior parietal (p = 0.018) and left insula (p = 0.04) regions. Further analyses with the corrected cortical thickness results and MoCA subscores showed a positive association between left superior parietal cortical thickness and Attention (p < 0.001). Conclusion: These findings suggest that correction procedures which account for brain atrophy and neurovascular lesions can significantly improve FS's segmentation results and reduce failure rates, thus maximizing power by preventing the loss of our important study participants. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and cognitive dysfunction due to neurodegenerative disease in the ONDRI study. [ABSTRACT FROM AUTHOR]

To quantify CSF transport kinetics and brain glymphatic distribution using MRI following intrathecal injection of gadolinium contrast in healthy adults. Eight completely healthy volunteer subjects underwent intrathecal injection of gadolinium via image guided lumbar puncture and serial MRI's at six subsequent time points up to 11 h post-injection. Rate of enhancement and deposition were calculated for various regions and lobes of the brain. Normalized cranial data revealed that gadolinium in the intracranial CSF spaces peaked within 1–3 h and started to decrease at 7 h. In some regions of the brain parenchyma, such as the cerebral cortex and white matter, enhancement was increasing after 11 h. Differential rates of uptake between the parietal and frontal (p = 0.0003), cingulate (p = 0.002) and temporal (p = 0.018) lobes were shown as well as a several fold change between various cortical regions. Lastly, a linear regression comparing laterality between 35 cortical regions yielded (R2 = 0.90, p < 0.001) with a slope of 1.01 showing symmetry in uptake. Gadolinium enhancement after lumbar intrathecal injection demonstrated differential CSF flow and brain parenchymal penetration, which illustrated the distributory function of the glymphatic system. • Serial MRI displays differential cerebral uptake of intrathecally injected gadolinium. • Uptake within the parietal lobe was reduced compared to other lobes as a function of time. • Variation of several fold in contrast kinetics was measured between regions of the cortex. [ABSTRACT FROM AUTHOR]

The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package.

Abstract Introduction Even though seasonal and sex‐dependent changes in hippocampal and subfield volumes are well known in animals, little is known about changes in humans. We hypothesized that changes in photoperiod would predict changes in hippocampal subfield volumes and that this association would be different between females and males. Methods A total of 10,033 participants ranging in age from 45 to 79 years were scanned by MRI in a single location as part of the UK Biobank project. Hippocampal subfield volumes were obtained using automated processing and segmentation algorithms using the developmental version of the FreeSurfer v 6.0. Photoperiod was defined as the number of hours between sunrise and sunset on the day of scan. Results Photoperiod correlated positively with total hippocampal volume and all subfield volumes across participants as well as in each sex individually, with females showing greater seasonal variation in a majority of left subfield volumes compared with males. ANCOVAs revealed significant differences in rate of change in only left subiculum, CA‐4, and GC‐ML‐DG between females and males. PLS showed highest loadings of hippocampal subfields in both females and males in GC‐ML‐DG, CA1, CA4, subiculum, and CA3 for left hemisphere and CA1, GC‐ML‐DG, CA4; subiculum and CA3 for right hemisphere in females; GC‐ML‐DG, CA1, subiculum, CA4 and CA3 for left hemisphere; CA1, GC‐ML‐DG, subiculum, CA4 and CA3 for right hemisphere in males. Conclusion The influence of day length on hippocampal volume has implications for modeling age‐related decline in memory in older adults, and sex differences suggest an important role for hormones in these effects.

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3–96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

Objective: To find out the optimal Brain Extraction Tool (BET) parameter (fractional intensity threshold) for measuring the brain volume compared with the standard manual method in our institute and to compare with those of automated FreeSurfer software Methods: This retrospective study was performed in 10 healthy adult subjects with data of 3D-T1W on 3T MR machine. The manual gold standard brain volume measurements were done by two independent readers. The automated segmentation using BET with varied parameters and FreeSurfer software were also performed. Then, the two automated methods were compared with the manual tracing to make the optimal parameter by seeking the highest intraclass correlation coefficient (ICC) using SPSS software. Results: The fractional intensity threshold for whole brain volume measurement of 0.1- 0.6 showed high ICC with the manual gold standard (ranging from 0.639 to 0.748). The best value was 0.1, showing highest ICC of 0.748 (p < 0.006) with confidence interval of 95% equal to (0.242; 0.932). There were no optimal parameters for right and left hippocampus volume measurement by BET due to very low ICC between BET and the reader (ICC ranging from 0.017 to 0.139 and from 0.012 to 0.110, for the right and left hippocampus volume, respectively). The ICC values of the automated FreeSurfer method with the manual tracing were also very low (0.063, 0.068, and 0.063 for right, left and bilateral hippocampi respectively). Conclusion: The optimal BET parameter (fractional intensity threshold) for automated brain volume measurement in our institute is 0.1 similar to the suggested value by prior study with high agreement (ICC=0.748) to the manual method.

Catatonia is a severe psychomotor syndrome that frequently occurs in patients with schizophrenia spectrum disorders (SSD). Accumulating neuroimaging evidence suggests orbitofrontal, frontoparietal and cerebellar network dysfunction in catatonia. Very little is known about contributions of brainstem regions (as part of the dopaminergic-based subcortical-cortical motor circuit) to catatonia in SSD patients. Here, we used structural magnetic resonance imaging (MRI) at 3 T to examine volumes of brainstem regions in catatonic SSD patients compared to non-catatonic SSD patients. Catatonia severity was measured with the Northoff Catatonia Rating Scale (NCRS). The segmentation of the brainstem in order to investigate the volumes of medulla oblongata, pons, superior cerebellar pedunculus, and midbrain was carried out using FreeSurfer vers. 6.0. Catatonic patients (NCRS total score ≥ 3; at least 1 point in the three different symptom categories; i.e., motor, behavioral, and affective; n = 30) had significantly smaller midbrain volumes (p = 0.004, Bonferroni corr.) when compared to non-catatonic patients (NCRS total score = 0; n = 29). In catatonic patients, significant correlations were detected between NCRS motor scores and whole brainstem (p = 0.015, Bonferroni corr.) volumes. These results support a neuromechanistically important role of brainstem structures in catatonia in SSD, particularly in motor symptom expression. [ABSTRACT FROM AUTHOR]

Introduction: Even though seasonal and sex‐dependent changes in hippocampal and subfield volumes are well known in animals, little is known about changes in humans. We hypothesized that changes in photoperiod would predict changes in hippocampal subfield volumes and that this association would be different between females and males. Methods: A total of 10,033 participants ranging in age from 45 to 79 years were scanned by MRI in a single location as part of the UK Biobank project. Hippocampal subfield volumes were obtained using automated processing and segmentation algorithms using the developmental version of the FreeSurfer v 6.0. Photoperiod was defined as the number of hours between sunrise and sunset on the day of scan. Results: Photoperiod correlated positively with total hippocampal volume and all subfield volumes across participants as well as in each sex individually, with females showing greater seasonal variation in a majority of left subfield volumes compared with males. ANCOVAs revealed significant differences in rate of change in only left subiculum, CA‐4, and GC‐ML‐DG between females and males. PLS showed highest loadings of hippocampal subfields in both females and males in GC‐ML‐DG, CA1, CA4, subiculum, and CA3 for left hemisphere and CA1, GC‐ML‐DG, CA4; subiculum and CA3 for right hemisphere in females; GC‐ML‐DG, CA1, subiculum, CA4 and CA3 for left hemisphere; CA1, GC‐ML‐DG, subiculum, CA4 and CA3 for right hemisphere in males. Conclusion: The influence of day length on hippocampal volume has implications for modeling age‐related decline in memory in older adults, and sex differences suggest an important role for hormones in these effects. [ABSTRACT FROM AUTHOR]

Objectives: Binocular depth inversion illusion (BDII), a visual, 'top–down'-driven information process, is impaired in schizophrenia and particularly in its early stages. BDII is a sensitive measure of impaired visual information processing and represents a valid diagnostic tool for schizophrenia and other psychotic disorders. However, neurobiological underpinnings of aberrant BDII in first-episode schizophrenia are largely unknown at present. Methods: In this study, 22 right-handed, first-episode, antipsychotic-naïve schizophrenia patients underwent BDII assessment and MRI scanning at 1.5 T. The surface-based analysis via new version of Freesurfer (6.0) enabled calculation of cortical thickness and surface area. BDII total and faces scores were related to the two distinct cortical measurements. Results: We found a significant correlation between BDII performance and cortical thickness in the inferior frontal gyrus and middle temporal gyrus (p < 0.003, Bonferroni corr.), as well as superior parietal gyrus, postcentral gyrus, supramarginal gyrus, and precentral gyrus (p < 0.05, CWP corr.), respectively. BDII performance was significantly correlated with surface area in the superior parietal gyrus and right postcentral gyrus (p < 0.003, Bonferroni corr.). Conclusion: BDII performance may be linked to cortical thickness and surface area variations in regions involved in "adaptive" or "top–down" modulation and stimulus processing, i.e., frontal and parietal lobes. Our results suggest that cortical features of distinct evolutionary and genetic origin differently contribute to BDII performance in first-episode, antipsychotic-naïve schizophrenia patients. [ABSTRACT FROM AUTHOR]

Catatonia is a central aspect of schizophrenia spectrum disorders (SSD) and most likely associated with abnormalities in affective, motor, and sensorimotor brain regions. However, contributions of different cortical features to the pathophysiology of catatonia in SSD are poorly understood. Here, T1-weighted structural magnetic resonance imaging data at 3 T were obtained from 56 right-handed patients with SSD. Using FreeSurfer version 6.0, we calculated cortical thickness, area, and local gyrification index (LGI). Catatonic symptoms were examined on the Northoff catatonia rating scale (NCRS). Patients with catatonia (NCRS total score ≥3; n = 25) showed reduced surface area in the parietal and medial orbitofrontal gyrus and LGI in the temporal gyrus (P <.05, corrected for cluster-wise probability [CWP]) as well as hypergyrification in rostral cingulate and medial orbitofrontal gyrus when compared with patients without catatonia (n = 22; P <.05, corrected for CWP). Following a dimensional approach, a negative association between NCRS motor and behavior scores and cortical thickness in superior frontal, insular, and precentral cortex was found (34 patients with at least 1 motor and at least 1 other affective or behavioral symptom; P <.05, corrected for CWP). Positive associations were found between NCRS motor and behavior scores and surface area and LGI in superior frontal, posterior cingulate, precentral, and pericalcarine gyrus (P <.05, corrected for CWP). The data support the notion that cortical features of distinct evolutionary and genetic origin differently contribute to catatonia in SSD. Catatonia in SSD may be essentially driven by cortex variations in frontoparietal regions including regions implicated in the coordination and goal-orientation of behavior. [ABSTRACT FROM AUTHOR]

Background: Manual width measurements of the middle cerebellar peduncle on MRI were shown to improve the accuracy of an imaging-guided diagnosis of multiple system atrophy (MSA). Recently, automated volume segmentation algorithms were able to reliably differentiate patients with Parkinson's disease (PD) and the parkinsonian variant of MSA. The objective of the current study was to integrate probabilistic information of the middle cerebellar peduncle into an existing MRI atlas for automated subcortical segmentation and to evaluate the diagnostic properties of the novel atlas for the differential diagnosis of MSA (parkinsonian and cerebellar variant) versus PD.Methods: Three Tesla MRI scans of 48 healthy individuals were used to establish an automated whole-brain segmentation procedure that includes the volumes of the putamen, cerebellar gray and white matter, and the middle cerebellar peduncles. Classification accuracy of segmented volumes were tested in early-stage MSA patients (18 MSA-parkinsonism, 13 MSA-cerebellar) and 19 PD patients using a C4.5 classifier.Results: Putaminal and infratentorial atrophy were present in 77.8% and 61.1% of MSA-parkinsonian patients, respectively. Four of 18 MSA-parkinsonian patients (22.2%) had infratentorial atrophy without evidence of putaminal atrophy. Infratentorial atrophy was present in all MSA-cerebellar patients, with concomitant putaminal atrophy in 46.2% of these cases. The diagnostic algorithm using putaminal and infratentorial volumetric information correctly classified all PD patients and 96.8% of MSA patients.Conclusions: The middle cerebellar peduncle was successfully integrated into a subcortical segmentation atlas, and its excellent diagnostic accuracy outperformed existing volumetric MRI processing strategies in differentiating MSA patients with variable atrophy patterns from PD patients. © 2019 International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Highlights • Study shows 44 brain regions volume changes with Alzheimer's disease. • Volumes were calculated both in absolute values and ratios to the whole brain volume. • The hippocampo-horn proportion is effective for hippocampal atrophy evaluation. • This method can be simplified for visual assessment. Abstract Ratios between opposing volumes from brain magnetic resonance imaging (MRI) can provide additional information to volumes in Alzheimer's disease (AD). Brain three-dimensional MPRAGE MRI at 3T were segmented into 44 regions using FreeSurfer v6 in 75 participants. The region's size in absolute volumes and relative proportions to the whole brain volume were compared between 39 AD patients and 36 age-, education- and sex-matched normal controls (NC). Volumes of the most atrophied parts were related to the opposing volumes of the most enlarged parts as ratios. The most atrophic structures in AD were both hippocampi. By contrast, the greatest enlargements in AD were inferior parts of both lateral ventricles. The best ratio for each side was the hippocampo-horn proportion calculated as ratio: the hippocampus / (the hippocampus + inferior lateral ventricle). Its optimal cut-off of 74% yielded sensitivity of 74% and specificity of 78% on the left and sensitivity of 74% and specificity of 78% on the right. The hippocampo-horn proportion is another measure to evaluate the degree of hippocampal atrophy on brain MRI in percentages. It has a potential to be simplified into a comparison of two-dimensional corresponding areas or a visual assessment. [ABSTRACT FROM AUTHOR]

Abstract Attempts to determine the cognitive benefits of adaptive working memory training in healthy adults has resulted in a polarized and stagnating field. Heterogeneous approaches and problematic methodological choices have contributed to the controversy. Structural neuroimaging has the potential to identify biological changes present after working memory training, which could go undetected by cognitive outcome tasks. In this study, 24 healthy community dwelling adults aged 18 – 40 years underwent structural magnetic resonance neuroimaging (MRI) before and after completing a 6-week n-back working memory training program. An active control group (n = 24) completed 6-weeks of processing speed training using the same computerized platform and neuroimaging protocol. Cortical surface area, thickness, and volume measurements were collected in a-priori regions of interest in the frontal and parietal lobes. Subcortical and total gray matter volumes were also measured. Group by time repeated measures Analyses of Variance (RM-ANOVAs) were conducted to identify changes in surface area, thickness, or volume measurements associated with training type over time. Null results were present across all neuroanatomical metrics after correction for multiple comparisons. Findings of this study, in concert with recently published investigations, support the idea that adaptive n-back working memory training is not an effective method for inducing neuroanatomical change. Further investigation into other forms of training may help advance the field. [ABSTRACT FROM AUTHOR]

Objectives: The clock drawing test (CDT) is one of the worldwide most used screening tests for Alzheimer's disease (AD). MRI studies have identified temporo-parietal regions being involved in CDT impairment. However, the contributions of specific hippocampal subfields and adjacent extrahippocampal structures to CDT performance in AD and mild cognitive impairment (MCI) have not been investigated so far. It is unclear whether morphological alterations or CDT score, or a combination of both, are able to predict AD. Methods: 38 AD patients, 38 MCI individuals and 31 healthy controls underwent neuropsychological assessment and MRI at 3 Tesla. FreeSurfer 5.3 was used to perform hippocampal parcellation. We used a collection of statistical methods to better understand the relationship between CDT and hippocampal formation. We also tested the clinical feasibility of this relationship when predicting AD. Results: Impaired CDT performance in AD was associated with widespread atrophy of the cornu ammonis, presubiculum, and subiculum, whereas MCI subjects showed CDT-related alterations of the CA4-dentate gyrus and subiculum. CDT correlates in AD and MCI showed regional and quantitative overlap. Importantly, CDT score was the best predictor of AD. Conclusions: Our findings lend support for an involvement of different hippocampal subfields in impaired CDT performance in AD and MCI. CDT seems to be more efficient than subfield imaging for predicting AD. [ABSTRACT FROM AUTHOR]