T1-weighted Brain Research Articles

White Matter Perivascular Space Burden Is Associated With Mild Traumatic Brain Injury History In Soldiers

Mild traumatic brain injury (mTBI) exposure in military Service Members may lead to impaired brain waste clearance which increases neurological disease risk including amyloidopathies. Perivascular spaces (PVS) are part of the glymphatic pathway which support brain waste clearance, especially during sleep. Greater magnetic resonance imaging (MRI)-visible PVS burden has been previously observed in patients with neurodegenerative diseases and animal TBI models. PURPOSE: To determine the association between PVS burden (number and volume) and mTBI history in Special Operations Forces (SOF) combat Soldiers. METHODS: In this cross-sectional study, participants underwent neuroimaging to capture T2-weighted (T2w) whole brain three dimensional volumes obtained on a Siemens 3T Biograph mMR or 3T MAGNETOM Prisma scanner (TR/TE = 3200/400ms, slice thickness = 1mm, FoV = 256mm x 256 mm). The PVS segmentation was performed using a previously validated, multi-scale deep convolutional encoder-decoder neural network. All T2w images were skull stripped and a custom T2w atlas with segmented cerebral white matter and subcortical gray matter labels was registered to each subject using Advanced Normalization Tools to minimize misidentifying thin tubular structures in the skull, brainstem, cerebellum, and cortex as PVS. Only PVS clusters within the white matter mask were quantified for analyses. Binary mTBI history was self-reported (“yes” or “no”). Due to non-normal PVS metric distribution, non-parametric Mann-Whitney U tests were used to determine group differences in PVS outcomes. RESULTS: In total, 223 healthy SOF combat Soldiers (age = 33.1±4.3yrs) were included for analysis and 123 (55.16%) reported mTBI history. Soldiers with mTBI history had greater PVS number (z = 2.506, P = 0.013) and PVS volume (z = 2.419, P = 0.016). CONCLUSIONS: We found mTBI history is associated with increased PVS burden in SOF combat Soldiers that are clinically recovered from mTBI. This may indicate ongoing physiological post-mTBI changes that could lead to impaired waste clearance via the glymphatic system. Future studies should determine how sleep influences PVS burden following mTBI exposure and if PVS number and volume are meaningful neurobiological outcomes for neurodegenerative disease risk. Funded by US Special Operations Command

School Climate, Cortical Structure, and Socioemotional Functioning: Associations across Family Income Levels.

School climates are important for children's socioemotional development and may also serve as protective factors in the context of adversity. Nevertheless, little is known about the potential neural mechanisms of such associations, as there has been limited research concerning the relation between school climate and brain structure, particularly for brain regions relevant for mental health and socioemotional functioning. Moreover, it remains unclear whether the role of school climate differs depending on children's socioeconomic status. We addressed these questions in baseline data for 9- to 10-year-olds from the Adolescent Brain and Cognitive Development study (analytic sample for socioemotional outcomes, n = 8887), conducted at 21 sites across the United States. Cortical thickness, cortical surface area, and subcortical volume were derived from T1-weighted brain magnetic resonance imaging. School climate was measured by youth report, and socioemotional functioning was measured by both youth and parent report. A positive school climate and higher family income were associated with lower internalizing and externalizing symptoms, with no evidence of moderation. There were no associations between school climate and cortical thickness or subcortical volume, although family income was positively associated with hippocampal volume. For cortical surface area, however, there was both a positive association with family income and moderation: There was an interaction between school climate and income for total cortical surface area and locally in the lateral orbitofrontal cortex. In all cases, there was an unexpected negative association between school climate and cortical surface area in the lower-income group. Consequently, although the school climate appears to be related to better socioemotional function for all youth, findings suggest that the association between a positive school environment and brain structure only emerges in the context of socioeconomic stress and adversity. Longitudinal data are needed to understand the role of these neural differences in socioemotional functioning over time.

Fairness-related performance and explainability effects in deep learning models for brain image analysis.

Purpose: Explainability and fairness are two key factors for the effective and ethical clinical implementation of deep learning-based machine learning models in healthcare settings. However, there has been limited work on investigating how unfair performance manifests in explainable artificial intelligence (XAI) methods, and how XAI can be used to investigate potential reasons for unfairness. Thus, the aim of this work was to analyze the effects of previously established sociodemographic-related confounders on classifier performance and explainability methods. Approach: A convolutional neural network (CNN) was trained to predict biological sex from T1-weighted brain MRI datasets of 4547 9- to 10-year-old adolescents from the Adolescent Brain Cognitive Development study. Performance disparities of the trained CNN between White and Black subjects were analyzed and saliency maps were generated for each subgroup at the intersection of sex and race. Results: The classification model demonstrated a significant difference in the percentage of correctly classified White male ( ) and Black male ( ) children. Conversely, slightly higher performance was found for Black female ( ) compared with White female ( ) children. Saliency maps showed subgroup-specific differences, corresponding to brain regions previously associated with pubertal development. In line with this finding, average pubertal development scores of subjects used in this study were significantly different between Black and White females ( ) and males ( ). Conclusions: We demonstrate that a CNN with significantly different sex classification performance between Black and White adolescents can identify different important brain regions when comparing subgroup saliency maps. Importance scores vary substantially between subgroups within brain structures associated with pubertal development, a race-associated confounder for predicting sex. We illustrate that unfair models can produce different XAI results between subgroups and that these results may explain potential reasons for biased performance.

An exploratory causal analysis of the relationships between the brain age gap and cardiovascular risk factors.

The brain age gap (BAG) has been shown to capture accelerated brain aging patterns and might serve as a biomarker for several neurological diseases. Moreover, it was also shown that it captures other biological information related to modifiable cardiovascular risk factors. Previous studies have explored statistical relationships between the BAG and cardiovascular risk factors. However, none of those studies explored causal relationships between the BAG and cardiovascular risk factors. In this work, we employ causal structure discovery techniques and define a Bayesian network to model the assumed causal relationships between the BAG, estimated using morphometric T1-weighted magnetic resonance imaging brain features from 2025 adults, and several cardiovascular risk factors. This setup allows us to not only assess observed conditional probability distributions of the BAG given cardiovascular risk factors, but also to isolate the causal effect of each cardiovascular risk factor on BAG using causal inference. Results demonstrate the feasibility of the proposed causal analysis approach by illustrating intuitive causal relationships between variables. For example, body-mass-index, waist-to-hip ratio, smoking, and alcohol consumption were found to impact the BAG, with the greatest impact for obesity markers resulting in higher chances of developing accelerated brain aging. Moreover, the findings show that causal effects differ from correlational effects, demonstrating the importance of accounting for variable relationships and confounders when evaluating the information captured by a biomarker. Our work demonstrates the feasibility and advantages of using causal analyses instead of purely correlation-based and univariate statistical analyses in the context of brain aging and related problems.

Segmentation of intrinsically very low contrast magnetic resonance brain images using tensor-based DTI registration

BackgroundIn patients with hypomyelinating leukodystrophies, contrast of T1-weighted brain MRI is very low due to the lack of myelin, preventing a reliable segmentation. In diffusion tensor images the contrast is higher, thanks to anisotropy and orientation of white matter (WM) tracts. We aimed to develop and assess a tensor-guided atlas-based segmentation method suitable for segmentation of very low contrast images. Methods17 control subjects (mean age 8.0 yrs (SD 8.0)) and 27 subjects with hypomyelinating leukodystrophies (mean age 10.7 yrs (SD 10.2)) were included. DTI and 3D T1 images were segmented using a DTI-TK tensor-guided IIT-atlas-based segmentation method. For the control subjects, these segmentations were compared with a conventional segmentation of their 3D T1-weighted images. A qualitative visual assessment and a quantitative assessment using DTI metrics was performed to assess the patient segmentations. ResultsIn control subjects, the tensor-based method performed as can be expected for atlas-based segmentation methods, with Dice coefficients of 0.65, 0.72, 0.81 and 0.86 for cortical grey matter (GM), WM, deep grey matter (DGM), and thalamus, respectively. In patients with hypomyelination the visual assessment showed anatomically adequate segmentations. All tissue-specific DTI metrics differed between patients and controls. Patients with hypomyelination had reduced FA and increased mean, axial and radial diffusivities, not only in total WM, but also in the corticospinal tracts, optic radiations and thalamus. ConclusionEven in the absence of normal myelin, the presence and direction of axons allowed tensor-based registration and thereby atlas-based segmentation. We showed the applicability of the segmentation method in the context of quantitative MRI, allowing for whole-brain or regional tissue-specific and tract-specific analyses of very low contrast images.

Multi-slice compressed sensing MRI reconstruction based on deep fusion connection network

Recently, magnetic resonance imaging (MRI) reconstruction based on deep learning has become popular. Nevertheless, reconstruction of highly undersampled MR images is still challenging due to severe aliasing effects. In this study we built a deep fusion connection network (DFCN) to efficiently utilize the correlation information between adjacent slices. The proposed method was evaluated with online public IXI dataset and Calgary-Campinas-359 dataset. The results show that DFCN can generate the best reconstruction images in de-aliasing and restoring tissue structure compared with several state-of-the-art methods. The mean value of the peak signal-to-noise ratio could reach 34.16 dB, the mean value of the structural similarity is 0.9626, and the mean value of the normalized mean square error is 0.1144 on T2-weighted brain data of IXI dataset under 10× acceleration. Additionally, the mean value of the peak signal-to-noise ratio could reach 30.17 dB, the mean value of the structural similarity is 0.9259, and the mean value of the normalized mean square error is 0.1294 on T1-weighted brain data of Calgary-Campinas-359 dataset under 10× acceleration. With the correlation information between adjacent slices as prior knowledge, our method can dramatically eliminate aliasing effects and enhance the reconstruction quality of undersampled MR images.

Corpus callosum anatomical changes in Alzheimer patients and the effect of acetylcholinesterase inhibitors on corpus callosum morphometry.

The Corpus Callosum (CC) is an important structure that includes the majority of fibers connecting the two brain hemispheres. Several neurodegenerative diseases may alter CC size and morphology leading to its atrophy and malfunction which may play a role in the pathological manifestations found in these diseases. The purpose of the current study is to determine any possible changes in CC size in patients suffering from Alzheimer's disease. The Study also investigated the effect of acetylcholinesterase inhibitors (AChEIs) on the size of CC and its association with improvement in the Alzheimer disease severity scores. Midsagittal size of CC were recorded prospectively from 439 routine T1-weighted MRI brain images in normal individuals. The internal skull surface was measured to calculate CC/ internal skull surface ratio. Two groups of patients were studied: 300 (150 male / 150 female) were healthy subjects and 130 (55 males / 75 females) had Alzheimer disease. Out of the 130 Alzheimer disease pateints, 70 patients were treated with Donepezil or Rivastigmine or both. The size of the CC was measured based on T1-weighted MRI images after the treatment to investigate any possible improvement in CC size. The mean surface area of CC in controls was 6.53±1.105 cm2. There was no significant difference between males and females (P < 0.627), and CC/ internal skull surface ratio was 4.41±0.77%. Patients with mild or severe Alzheimer disease showed a significant reduction in CC size compared to healthy controls. Treating mild Alzheimer patients with either Donepezil or Rivastigmine exerts a comparable therapeutic effect in improving the CC size. There was more improvement in the size of CC in patients with severe Alzheimer disease by using combined therapy of Donepezil and Rivastigmine than using single a medication. we measured the mean size of the various portions of the corpus callosum in normal individuals and Alzheimer patients before and after taking Donepezil and Rivastigmine. Alzheimer patients have pronounced reduction in CC which is corrected after taking Donepezil and Rivastigmine leading to remarkable improvement in Alzheimer disease severity scores.

Differences in gut microbiota correlate with symptoms and regional brain volumes in patients with late-life depression.

Depression is associated with gut dysbiosis that disrupts a gut-brain bidirectional axis. Gray matter volume changes in cortical and subcortical structures, including prefrontal regions and the hippocampus, have also been noted in depressive disorders. However, the link between gut microbiota and brain structures in depressed patients remains elusive. Neuropsychiatric measures, stool samples, and structural brain images were collected from 36 patients with late-life depression (LLD) and 17 healthy controls. 16S ribosomal RNA (rRNA) gene sequencing was used to profile stool microbial communities for quantitation of microbial composition, abundance, and diversity. T1-weighted brain images were assessed with voxel-based morphometry to detect alterations in gray matter volume between groups. Correlation analysis was performed to identify the possible association between depressive symptoms, brain structures and gut microbiota. We found a significant difference in the gut microbial composition between patients with late-life depression (LLD) and healthy controls. The genera Enterobacter and Burkholderia were positively correlated with depressive symptoms and negatively correlated with brain structural signatures in regions associated with memory, somatosensory integration, and emotional processing/cognition/regulation. Our study purports the microbiota-gut-brain axis as a potential mechanism mediating the symptomatology of LLD patients, which may facilitate the development of therapeutic strategies targeting gut microbes in the treatment of elderly depressed patients.

Percentage amplitude of fluctuation and structural covariance changes of subjective cognitive decline in patients: A multimodal imaging study.

Back groundSubjective cognitive decline (SCD) may be the first clinical sign of Alzheimer’s disease (AD). The possible neural mechanisms of SCD are not well known. This study aimed to compare percent amplitude of fluctuation (PerAF) and structural covariance patterns in patients with SCD and healthy controls (HCs).MethodsWe enrolled 53 patients with SCD and 65 HCs. Resting-state functional magnetic resonance imaging (MRI) data and T1-weighted anatomical brain 3.0-T MRI scans were collected. The PerAF approach was applied to distinguish altered brain functions between the two groups. A whole-brain voxel-based morphometry analysis was performed, and all significant regions were selected as regions of interest (ROIs) for the structural covariance analysis. Statistical analysis was performed using two-sample t-tests, and multiple regressions were applied to examine the relationships between neuroimaging findings and clinical symptoms.ResultsFunctional MRI results revealed significantly increased PerAF including the right hippocampus (HIPP) and right thalamus (THA) in patients with SCD relative to HCs. Gray matter volume (GMV) results demonstrated decreased GMV in the bilateral ventrolateral prefrontal cortex (vlPFC) and right insula in patients with SCD relative to HCs. Taking these three areas including the bilateral vlPFC and right insula as ROIs, differences were observed in the structural covariance of the ROIs with several regions between the two groups. Additionally, significant correlations were observed between neuroimaging findings and clinical symptoms.ConclusionOur study investigated the abnormal PerAF and structural covariance patterns in patients with SCD, which might provide new insights into the pathological mechanisms of SCD.

The Utility of Early Brain MRI for Patients with Neurofibromatosis Type 1 and Optic Pathway Glioma: A Long-Term Follow-Up in a Tertiary Referral Hospital.

Screening studies have shown detection of optic pathway gliomas (OPGs) in 8 to 31% of children with neurofibromatosis type 1 (NF1). Many of those affected show prolonged indolent phases, but others develop vision disturbances even before diagnosis and treatment. We assessed the clinical presentation at diagnosis, location, natural progression, and risk factors for impaired vision of OPG. The clinical database of the NF1 multidisciplinary clinic of Schneider Children's Medical Center of Israel was reviewed for all patients diagnosed and followed with NF1 during 2007 to 2019. OPG was diagnosed by hyperintensity and thickening along the optic pathway on T2-weighted brain magnetic resonance imaging (MRI), with or without contrast enhancement. Of 257 children with NF1 who underwent MRI, 57 (22%) were diagnosed with OPG; 31 (54%) were females. Twenty-five (44%) had familial NF1. Fifteen (26%) who exhibited tumor progression and worsening in ophthalmic examinations required treatment. Post-chiasmatic glioma was a predictive factor for treatment (p < 0.05), whereas MRI done later and female gender were not significant. Four patients who eventually needed therapy had normal ophthalmic examinations at least 1 year prior to their first MRI. For 6 (40%) of the patients treated, vision continued to worsen. Our findings demonstrate that normal ophthalmic examinations do not always exclude OPG in children with NF1. Early brain MRI before age 36 months may detect OPG, lead to better follow-up and early treatment, and help improve vision outcome.

Brain structural and perfusion changes in amyotrophic lateral sclerosis-frontotemporal dementia patients with cognitive and motor onset: a preliminary study

Amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD) is rare but exhibits worse prognosis than either ALS or FTD alone. However, cognitive onset ALS-FTD (ALS-FTD-C) confers significantly better patient survival than does motor onset ALS-FTD (ALS-FTD-M), underscoring a meager understanding of pathological group differences. This study aimed to assess disparities in cortical atrophy and perfusion shown by patients with the above disease variants. A total of 38 participants (ALS-FTD-C, 8; ALS-FTD-M, 6; simultaneous-onset ALS-FTD [ALS-FTD-S], 4; healthy controls [HC], 20) qualified for the study and underwent magnetic resonance imaging scan. Three-dimensional T1-weighted structural brain imaging and 3D-pseudocontinuous arterial spin-labeled imaging were routinely collected. Gray matter volume (GMV) and cerebral blood flow (CBF) in ALS-FTD-C and ALS-FTD-M were compared through voxel-based analysis. Correlations between imaging parameters and clinical data were also assessed. Compared with HC, ALS-FTD had significant GMV reduction mainly in bilateral limbic system. GMV reduction in ALS-FTD-C was similar in pattern but less widespread, whereas ALS-FTD-M lacked any significant GMV reduction. In CBF analyses, ALS-FTD displayed hypoperfusion in bilateral motor cortex, frontotemporal lobe, and left basal ganglia. Hypoperfusion involved bilateral temporal lobe, prefrontal cortex, and putamen in ALS-FTD-C but was limited to left parahippocampal gyrus in ALS-FTD-M. Correlations between clinical data and GMV/CBF changes in specific regions were also identified in ALS-FTD. Group-specific patterns of cortical atrophy and perfusion were evident in ALS-FTD-C and ALS-FTD-M. ALS-FTD-C showed pronounced cortical atrophy and hypoperfusion, which were otherwise minimal in ALS-FTD-M. Above findings preliminarily revealed the pathological group differences that may help in classifying patients with ALS-FTD.

The cortical thickness of the area PF of the left inferior parietal cortex mediates technical-reasoning skills

Most recent research highlights how a specific form of causal understanding, namely technical reasoning, may support the increasing complexity of tools and techniques developed by humans over generations, i.e., the cumulative technological culture (CTC). Thus, investigating the neurocognitive foundations of technical reasoning is essential to comprehend the emergence of CTC in our lineage. Whereas functional neuroimaging evidence started to highlight the critical role of the area PF of the left inferior parietal cortex (IPC) in technical reasoning, no studies explored the links between the structural characteristics of such a brain region and technical reasoning skills. Therefore, in this study, we assessed participants’ technical-reasoning performance by using two ad-hoc psycho-technical tests; then, we extracted from participants’ 3 T T1-weighted magnetic-resonance brain images the cortical thickness (i.e., a volume-related measure which is associated with cognitive performance as reflecting the size, density, and arrangement of cells in a brain region) of all the IPC regions for both hemispheres. We found that the cortical thickness of the left area PF predicts participants’ technical-reasoning performance. Crucially, we reported no correlations between technical reasoning and the other IPC regions, possibly suggesting the specificity of the left area PF in generating technical knowledge. We discuss these findings from an evolutionary perspective, by speculating about how the evolution of parietal lobes may have supported the emergence of technical reasoning in our lineage.

Persistent Non-pharmacological Pain Management and Brain-Predicted Age Differences in Middle-Aged and Older Adults With Chronic Knee Pain.

Chronic pain has been associated with changes in pain-related brain structure and function, including advanced brain aging. Non-pharmacological pain management is central to effective pain management. However, it is currently unknown how use of non-pharmacological pain management is associated with pain-related brain changes. The objective of the current study was to examine the association between brain-predicted age difference and use of non-pharmacological pain management (NPM) in a sample of middle-aged and older adults with and without chronic knee pain across two time points. One-hundred and 12 adults (mean age = 57.9 ± 8.2 years) completed sociodemographic measures, clinical pain measures, structural T1-weighted brain magnetic resonance imaging, and self-reported non-pharmacological pain management. Using a validated approach, we estimated a brain-predicted age difference (brain-PAD) biomarker, calculated as brain-predicted age minus chronological age, and the change in brain-PAD across 2 years. Repeated measures analysis of covariance was conducted to determine associations of non-pharmacological pain management and brain-PAD, adjusting for age, sex, study site, and clinical pain. There was a significant time*pain/NPM interaction effect in brain-PAD (p < 0.05). Tests of simple main effects indicated that those persistently using NPM had a “younger” brain-PAD over time, suggesting a potential protective factor in persistent NPM use. Future studies are warranted to determine the influence of NPM in brain aging and pain-related neurological changes.

Basic lifestyle habits and volume change in total gray matter among community dwelling middle-aged and older Japanese adults

The brain controls human behavior, and the gray matter is the main resource of neuronal cells. We examined the longitudinal relationship between six basic lifestyle habits (diet, exercise, sleep, alcohol consumption, smoking, and social activity including employment) and total gray matter volume in community-dwelling adults in Japan.This two-year follow-up study with data derived from the National Institute for Longevity Sciences, Longitudinal Study of Aging, Aichi, Japan, included adults aged 40–87 years (n = 1665, men: 51%). Lifestyle habits were assessed at baseline (2008–2010) using self-reported questionnaires and three-day dietary records. Total gray matter volume at baseline and after two years was estimated using T1-weighted brain magnetic resonance imaging and FreeSurfer software. The association between each lifestyle factor, the total number of healthy lifestyle habits, and gray matter volume change was determined via a multiple linear regression analysis adjusting for baseline age, total gray matter volume, and other confounders. The mean ± standard deviation decrease in total gray matter volume during the two-year follow-up period was 0.94 ± 1.86% in men and 0.61 ± 2.27% in women. In the multiple regression analysis, volume loss in total gray matter positively correlated with male smoking, while it was negatively correlated with male social activity and employment, female dietary diversity, and the total number of healthy lifestyle habits (standardized beta coefficient; −0.061 in men [p = 0.07], −0.113 in women [p < 0.05]). Therefore, engaging in social activities, non-smoking, a diverse diet, or adopting one healthy lifestyle habit may help prevent gray matter volume loss.

Development of high quality T1-weighted and diffusion tensor templates of the older adult brain in a common space

High-quality T1-weighted (T1w) and diffusion tensor imaging (DTI) brain templates that are representative of the individuals under study enhance the accuracy of template-based neuroimaging investigations, and when they are also located in a common space they facilitate optimal integration of information on brain morphometry and diffusion characteristics. However, such multimodal templates have not been constructed for the brain of older adults. The purpose of this work was threefold: (A) to introduce an iterative method for construction of multimodal T1w and DTI templates that aims at maximizing the quality of each template separately as well as the spatial matching between templates, (B) to use this method to develop T1w and DTI templates of the older adult brain in a common space, and (C) to evaluate the performance of the method across iterations and compare it to the performance of state-of-the-art approaches based on multichannel registration. It was demonstrated that more iterations of the proposed method enhanced the characteristics and spatial matching of the resulting T1w and DTI templates. The templates of the older adult brain generated by the final iteration of the proposed method provided better delineation of brain structures, higher discriminability between tissues, and higher image sharpness near the cortex compared to templates generated with approaches employing multichannel registration. In addition, the spatial matching between the T1w and DTI templates constructed by the proposed method approximated the template alignment achieved with methods employing multichannel registration. Finally, when using the templates generated by the proposed method as references for spatial normalization of older adult T1w and DTI data, both the intra-modality inter-subject normalization precision and the inter-modality spatial matching were higher in most metrics than those achieved with templates constructed with other methods. Overall, the present work brought new insights into multimodal template construction, generated much-needed high quality T1w and DTI templates of the older adult brain in a common space, and conducted a thorough, quantitative evaluation of available multimodal template construction methods.

Association Between Vision and Brain Cortical Thickness in a Community-Dwelling Elderly Cohort.

PurposeVisual impairment is a major cause of disability and impairment of cognitive function in older people. Brain structural changes associated with visual function impairment are not well understood. The objective of this study was to assess the association between visual function and cortical thickness in older adults.MethodsParticipants were selected from the French population-based ESPRIT cohort of 2259 community-dwelling adults ≥65 years old enrolled between 1999 and 2001. We considered visual function and brain MRI images at the 12-year follow-up in participants who were right-handed and free of dementia and/or stroke, randomly selected from the whole cohort. High-resolution structural T1-weighted brain scans acquired with a 3-Tesla scanner. Regional reconstruction and segmentation involved using the FreeSurfer image-analysis suite.ResultsA total of 215 participants were included (mean [SD] age 81.8 [3.7] years; 53.0% women): 30 (14.0%) had central vision loss and 185 (86.0%) normal central vision. Vision loss was associated with thinner cortical thickness in the right insula (within the lateral sulcus of the brain) as compared with the control group (mean thickness 2.38 [0.04] vs 2.50 [0.03] mm, 4.8% thinning, pcorrected= 0.04) after adjustment for age, sex, lifetime depression and cardiovascular disease.ConclusionThe present study describes a significant thinning of the right insular cortex in older adults with vision loss. The insula subserves a wide variety of functions in humans ranging from sensory and affective processing to high-level cognitive processing. Reduced insula thickness associated with vision loss may increase cognitive burden in the ageing brain.

Cerebral Arterial Growth in Childhood

BackgroundImproved understanding of cerebral arterial growth in children may lead to advances in the diagnosis and treatment of pediatric cerebrovascular disease. We correlated cross-sectional diameters of major cerebral arterial structures with age, sex, head circumference, weight, and height in children without cerebrovascular disease. MethodsChildren with normal brain magnetic resonance imaging (MRI) were retrospectively identified and stratified into 23 age cohorts from birth to age 18 years. Absence of vascular disease was verified by medical record review. Demographic and biometric data were obtained from medical records. Intracranial arterial diameter (IAD) was measured on T2-weighted fast spin echo brain MRI of vertebral, basilar, internal carotid artery, and circle of Willis arterial segments. ResultsA total of 307 subjects are included in the analysis, including 5833 vessel segments (mean age 8.4 years, 53% female). Indications for imaging were headache (73%), seizure (26%) and concussion (1%). IAD rapidly increased during the first year of life (mean growth velocity 0.064 to 0.213 mm/month) and then plateaued or slightly decreased between age one and 18 years (mean growth velocity −0.002 to 0.003 mm/month). Multivariable analysis shows strongest correlation with head circumference as a predictor of IAD. Weaker correlations are associated with weight and age. Height and sex are not well correlated with IAD. ConclusionsIntracranial arteries grow rapidly during the first year of life and then sharply plateau or slightly decrease in luminal diameter between infancy and early adulthood. IAD is more closely correlated with head circumference than age.

Analyses of n-back high-load working memory in early Parkinson's disease: An fMRI study

BackgroundCognitive impairment is common in Parkinson's disease (PD). Working memory is a group of basic processes that is part of many cognitive operations (from complex decision-making to selective attention). To date, there is not sufficient research on working memory impairment in early PD patients, especially patients under high load. AimThe aim of this study was to observe the activation of brain regions in early PD patients during verbal high-load cognitive tasks and to explore the correlation between cognitive behavior indexes and gray matter areas of brain structure. MethodsA total of 45 participants were included: 15 healthy controls and 30 PD patients without dementia. They underwent BOLD imaging during a digital N-back working memory task in a 3 T scanner, and their high-resolution T1-weighted three-dimensional brain anatomical images were collected. The SPM12 standard method was used to generate a comparison map with 2 plus 3-back greater than 0-back, and with age, sex, levodopa equivalent daily dose (LEDD) and education as covariables, and multiple regression analysis was conducted to analyze the correlation between MOCA, MES and brain gray matter volume. ResultsCompared with the control group, the frontoparietal region was significantly activated in patients with PD under the background of high working memory burden. In addition, there was a widespread area correlation between the brain structure and basic cognitive function. ConclusionsThe frontoparietal cortex is the core hub of working memory, and there may be a compensation mechanism in PD patients without dementia with a high-load of working memory.

Deep transfer learning-based fully automated detection and classification of Alzheimer's disease on brain MRI.

To employ different automated convolutional neural network (CNN)-based transfer learning (TL) methods for both binary and multiclass classification of Alzheimer's disease (AD) using brain MRI. Herein, we applied three popular pre-trained CNN models (ResNet101, Xception, and InceptionV3) using a fine-tuned approach of TL on 3D T1-weighted brain MRI from a subset of ADNI dataset (n = 305 subjects). To evaluate power of TL, the aforementioned networks were also trained from scratch for performance comparison. Initially, Unet network segmentedthe MRI scans into characteristic components of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The proposed networks were trained and tested over the pre-processed and augmented segmented and whole images for both binary (NC/AD + progressive mild cognitive impairment (pMCI)+stable MCI (sMCI)) and 4-class (AD/pMCI/sMCI/NC) classification. Also, two independent test sets from the OASIS (n = 30) and AIBL (n = 60) datasets were used to externally assess the performance of the proposed algorithms. The proposed TL-based CNN models achieved better performance compared to the training CNN models from scratch. On the ADNI test set, InceptionV3-TL achieved the highest accuracy of 93.75% and AUC of 92.0% for binary classification, as well as the highest accuracy of 93.75% and AUC of 96.0% for multiclass classification of AD on the whole images. On the OASIS test set, InceptionV3-TL outperformed two other models by achieving 93.33% accuracy with 93.0% AUC in binary classification of AD on the whole images. On the AIBL test set, InceptionV3-TL also outperformed two other models in both binary and multiclass classification tasks on the whole MR images and achieved accuracy/AUC of 93.33%/95.0% and 90.0%/93.0%, respectively. The GM segment as input provided the highest performance in both binary and multiclass classification of AD, as compared to the WM and CSF segments. This study demonstrates the potential of applying deep TL approach for automated detection and classification of AD using brain MRI with high accuracy and robustness across internal and external test data, suggesting that these models can possibly be used as a supportive tool to assist clinicians in creating objective opinion and correct diagnosis. We used CNN-based TL approaches and the augmentation techniques to overcome the insufficient data problem. Our study provides evidence that deep TL algorithms can be used for both binary and multiclass classification of AD with high accuracy.

Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction

Objective. Machine Learning methods can learn how to reconstruct magnetic resonance images (MRI) and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physics-informed networks incorporate the forward model of accelerated MRI reconstruction in the learning process. With increasing network complexity, robustness is not ensured when reconstructing data unseen during training. We aim to embed data consistency (DC) in deep networks while balancing the degree of network complexity. While doing so, we will assess whether either explicit or implicit enforcement of DC in varying network architectures is preferred to optimize performance. Approach. We propose a scheme called Cascades of Independently Recurrent Inference Machines (CIRIM) to assess DC through unrolled optimization. Herein we assess DC both implicitly by gradient descent and explicitly by a designed term. Extensive comparison of the CIRIM to compressed sensing as well as other Machine Learning methods is performed: the End-to-End Variational Network (E2EVN), CascadeNet, KIKINet, LPDNet, RIM, IRIM, and UNet. Models were trained and evaluated on T1-weighted and FLAIR contrast brain data, and T2-weighted knee data. Both 1D and 2D undersampling patterns were evaluated. Robustness was tested by reconstructing 7.5× prospectively undersampled 3D FLAIR MRI data of multiple sclerosis (MS) patients with white matter lesions. Main results. The CIRIM performed best when implicitly enforcing DC, while the E2EVN required an explicit DC formulation. Through its cascades, the CIRIM was able to score higher on structural similarity and PSNR compared to other methods, in particular under heterogeneous imaging conditions. In reconstructing MS patient data, prospectively acquired with a sampling pattern unseen during model training, the CIRIM maintained lesion contrast while efficiently denoising the images. Significance. The CIRIM showed highly promising generalization capabilities maintaining a very fair trade-off between reconstructed image quality and fast reconstruction times, which is crucial in the clinical workflow.