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Abstract Background New daily persistent headache (NDPH) is a rare primary headache with unclear pathogenesis. Neuroimaging studies of NDPH are limited, and controversy still exists. Diffusion tensor imaging (DTI) is commonly used to study the white matter. However, lacking specificity, the potential pathological mechanisms of white matter microstructural changes remain poorly understood. In addition, the intricacy of gray matter structures impedes the application of the DTI model. Here, we applied an advanced diffusion model of neurite orientation dispersion and density imaging (NODDI) to study the white matter and cortical gray matter microstructure in patients with NDPH. Methods This study assessed brain microstructure, including 27 patients with NDPH, and matched 28 healthy controls (HCs) by NODDI. The differences between the two groups were assessed by tract-based spatial statistics (TBSS) and surface-based analysis (SBA), focusing on the NODDI metrics (neurite density index (NDI), orientation dispersion index (ODI), and isotropic volume fraction (ISOVF)). Furthermore, we performed Pearson’s correlation analysis between the NODDI indicators and clinical characteristics. Results Compared to HCs, patients with NDPH had a reduction of density and complexity in several fiber tracts. For robust results, the fiber tracts were defined as comprising more than 100 voxels, including bilateral inferior fronto-occipital fasciculus (IFOF), left superior longitudinal fasciculus (SLF) and inferior longitudinal fasciculus (ILF), as well as right corticospinal tract (CST). Moreover, the reduction of neurite density was uncovered in the left superior and middle frontal cortex, left precentral cortex, and right lateral orbitofrontal cortex and insula. There was no correlation between the NODDI metrics of these brain regions and clinical variables or scales of relevance after the Bonferroni correction. Conclusions Our research indicated that neurite loss was detected in both white matter and cortical gray matter of patients with NDPH.

Background: New daily persistent headache (NDPH) is a rare primary headache with unclear pathogenesis. Neuroimaging studies of NDPH are limited, and controversy still exists. Diffusion tensor imaging (DTI) is commonly used to study the white matter. However, lacking specificity, the potential pathological mechanisms of white matter microstructural changes remain poorly understood. In addition, the intricacy of gray matter structures impedes the application of the DTI model. Here, we applied an advanced diffusion model of neurite orientation dispersion and density imaging (NODDI) to study the white matter and cortical gray matter microstructure in patients with NDPH. Methods: This study assessed brain microstructure, including 27 patients with NDPH, and matched 28 healthy controls (HCs) by NODDI. The differences between the two groups were assessed by tract-based spatial statistics (TBSS) and surface-based analysis (SBA), focusing on the NODDI metrics (neurite density index (NDI), orientation dispersion index (ODI), and isotropic volume fraction (ISOVF)). Furthermore, we performed Pearson's correlation analysis between the NODDI indicators and clinical characteristics. Results: Compared to HCs, patients with NDPH had a reduction of density and complexity in several fiber tracts. For robust results, the fiber tracts were defined as comprising more than 100 voxels, including bilateral inferior fronto-occipital fasciculus (IFOF), left superior longitudinal fasciculus (SLF) and inferior longitudinal fasciculus (ILF), as well as right corticospinal tract (CST). Moreover, the reduction of neurite density was uncovered in the left superior and middle frontal cortex, left precentral cortex, and right lateral orbitofrontal cortex and insula. There was no correlation between the NODDI metrics of these brain regions and clinical variables or scales of relevance after the Bonferroni correction. Conclusions: Our research indicated that neurite loss was detected in both white matter and cortical gray matter of patients with NDPH. [ABSTRACT FROM AUTHOR]

Background: Numerous studies have proposed that periodontitis is a potential risk factor for Alzheimer's disease. However, the association between periodontitis and brain normal cognition in aged and elderly individuals (NCs) is unclear. Such a link could provide clues to Alzheimer's disease development and strategies for early prevention. Objective: To explore the associations between periodontal condition and metrics of both brain structure and function among NCs with the help of multimodal magnetic resonance imaging (MRI). Methods: High‐resolution T1‐weighted structural data, resting‐state functional‐MRI data, and measures of periodontal condition were collected from 40 NCs. Cortical volume, thickness, and area as well as regional homogeneity were calculated with the aid of DPABISurf software. Correlation analyses were then conducted between each imaging metric and periodontal index. Results: Consistent negative correlations were observed between severity of periodontitis (mild, moderate, severe) and cortical volume, area, and thickness, not only in brain regions that took charge of primary function but also in brain regions associated with advanced cognition behavior. Among participants with mild attachment loss (AL) and a shallow periodontal pocket depth (PPD), periodontal index was positively correlated with most measures of brain structure and function, while among participants with severe AL and deep PPD, periodontal index was negatively correlated with measures of brain structure and function (all p <.005 for each hemisphere). Conclusions: Our results demonstrate that periodontitis is associated with widespread changes in brain structure and function among middle‐aged and elderly adults without signs of cognitive decline, which might be a potential risk factor for brain damage. [ABSTRACT FROM AUTHOR]

Overlapping parkinsonism, cerebellar ataxia, and pyramidal signs render challenges in the clinical diagnosis of multiple system atrophy (MSA). The neuroimaging pattern is valuable to understand its pathophysiology and improve its diagnostic effect. We retrospectively obtained magnetic resonance imaging and susceptibility-weighted imaging in patients with MSA (including parkinsonian type [MSA-P] and cerebellar type [MSA-C]), Parkinson's disease, and normal controls. We quantified neuroimaging features to identify the optimal threshold for diagnosis. Furthermore, we explore neuroimaging patterns of MSA by mapping the subcortical morphological alterations and constructing a diagnostic model. Compared to controls, normalized putaminal volume significantly decreased in patients with MSA-P (P <.001) and normalized pontine volume significantly decreased in patients with MSA-C (P <.001). The Youden index of the threshold-based clinical prediction model was 0.871-0.928 in patients with MSA. The neuroimaging pattern in patients with MSA was jointly located in the lateral putamen, and the neuroimaging pattern prediction model achieved a classification accuracy of 83.9%-100%. The quantitative neuroimaging features and surface-based morphologic anomalies represent the markers of MSA and open new avenues for personalized clinical diagnosis. • Nonspecific symptoms render the clinical diagnosis of MSA challenging. • Quantitative neuroimaging features of MSA are conducive to clinical diagnosis. • Lateral putaminal abnormalities are the neuroimaging pattern of MSA-P. [ABSTRACT FROM AUTHOR]

Background: Functional constipation (FCon) is a common functional gastrointestinal disorder (FGID). Studies have indicated a higher likelihood of psychiatric disorders, such as anxiety, depression, sleep disturbances, and impaired concentration, among patients with FCon. However, the underlying pathophysiological mechanisms responsible for these symptoms in FCon patients remain to be fully elucidated. The human brain is a complex network architecture with several fundamental organizational properties. Neurological interactions between gut symptoms and psychiatric issues may be closely associated with these complex networks. Methods: In the present study, a total of 35 patients with FCon and 40 healthy controls (HC) were recruited for a series of clinical examinations and restingstate functional magnetic imaging (RS-fMRI). We employed the surface-based analysis (SBA) approach, utilizing the Schaefer cortical parcellation template and Tikhonov regularization. Graph theoretical analysis (GTA) and functional connectivity (FC) analysis of RS-fMRI were conducted to investigate the aberrant network alterations between the two groups. Additionally, correlation analyses were performed between the network indices and clinical variables in patients with FCon. Results: At the global level, we found altered topological properties and networks in patients with FCon, mainly including the significantly increased clustering coefficient (CP), local efficiency (Eloc), and shortest path length (LP), whereas the decreased global efficiency (Eglob) compared to HC. At the regional level, patients with FCon exhibited increased nodal efficiency in the frontoparietal network (FPN). Furthermore, FC analysis demonstrated several functional alterations within and between the Yeo 7 networks, particularly including visual network (VN), limbic network (LN), default mode network (DMN), and somatosensory-motor network (SMN) in sub-network and large-scale network analysis. Correlation analysis revealed that there were no significant associations between the network metrics and clinical variables in the present study. Conclusion: These results highlight the altered topological architecture of functional brain networks associated with visual perception abilities, emotion regulation, sensorimotor processing, and attentional control, which may contribute to effectively targeted treatment modalities for patients with FCon. [ABSTRACT FROM AUTHOR]

BackgroundLate-onset Alzheimer's disease (AD) is characterized by primary memory impairment, which then progresses towards severe deficits across cognitive domains. Here, we report how performance in cognitive domains relates to patterns of tau deposition and cortical thickness.MethodsWe analyzed data from 131 amyloid-β positive participants (55 cognitively normal, 46 mild cognitive impairment, 30 AD) of the Alzheimer's Disease Neuroimaging Initiative who underwent magnetic resonance imaging (MRI), flortaucipir (FTP) positron emission tomography, and neuropsychological testing. Surface-based vertex-wise and region-of-interest analyses were conducted between FTP and cognitive test scores, and between cortical thickness and cognitive test scores.ResultsFTP and thickness were differentially related to cognitive performance in several domains. FTP-cognition associations were more widespread than thickness-cognition associations. Further, FTP-cognition patterns reflected cortical systems that underlie different aspects of cognition.ConclusionsOur findings indicate that AD-related decline in domain-specific cognitive performance reflects underlying progression of tau and atrophy into associated brain circuits. They also suggest that tau-PET may have better sensitivity to this decline than MRI-derived measures of cortical thickness.

Introduction: Analysis of task fMRI studies is typically based on using ordinary least squares within a voxel- or vertex-wise linear regression framework known as the general linear model. This use produces estimates and standard errors of the regression coefficients representing amplitudes of task-induced activations. To produce valid statistical inferences, several key statistical assumptions must be met, including that of independent residuals. Since task fMRI residuals often exhibit temporal autocorrelation, it is common practice to perform "prewhitening" to mitigate that dependence. Prewhitening involves estimating the residual correlation structure and then applying a filter to induce residual temporal independence. While theoretically straightforward, a major challenge in prewhitening for fMRI data is accurately estimating the residual autocorrelation at each voxel or vertex of the brain. Assuming a global model for autocorrelation, which is the default in several standard fMRI software tools, may under- or over-whiten in certain areas and produce differential false positive control across the brain. The increasing popularity of multiband acquisitions with faster temporal resolution increases the challenge of effective prewhitening because more complex models are required to accurately capture the strength and structure of autocorrelation. These issues are becoming more critical now because of a trend toward subject-level analysis and inference. In group-average or group-difference analyses, the within-subject residual correlation structure is accounted for implicitly, so inadequate prewhitening is of little real consequence. For individual subject inference, however, accurate prewhitening is crucial to avoid inflated or spatially variable false positive rates. Methods: In this paper, we first thoroughly examine the patterns, sources and strength of residual autocorrelation in multiband task fMRI data. Second, we evaluate the ability of different autoregressive (AR) model-based prewhitening strategies to effectively mitigate autocorrelation and control false positives. We consider two main factors: the choice of AR model order and the level of spatial regularization of AR model coefficients, ranging from local smoothing to global averaging. We also consider determining the AR model order optimally at every vertex, but we do not observe an additional benefit of this over the use of higher-order AR models (e.g. (AR(6)). To overcome the computational challenge associated with spatially variable prewhitening, we developed a computationally efficient R implementation using parallelization and fast C++ backend code. This implementation is included in the open source R package BayesfMRI. Results: We find that residual autocorrelation exhibits marked spatial variance across the cortex and is influenced by many factors including the task being performed, the specific acquisition protocol, mis-modeling of the hemodynamic response function, unmodeled noise due to subject head motion, and systematic individual differences. We also find that local regularization is much more effective than global averaging at mitigating autocorrelation. While increasing the AR model order is also helpful, it has a lesser effect than allowing AR coefficients to vary spatially. We find that prewhitening with an AR(6) model with local regularization is effective at reducing or even eliminating autocorrelation and controlling false positives. Conclusion: Our analysis revealed dramatic spatial differences in autocorrelation across the cortex. This spatial topology is unique to each session, being influenced by the task being performed, the acquisition technique, various modeling choices, and individual differences. If not accounted for, these differences will result in differential false positive control and power across the cortex and across subjects. [ABSTRACT FROM AUTHOR]

Surface-based analysis of magnetic resonance imaging (MRI) data of the brain plays an important role in clinical and research applications. To achieve accurate three-dimensional (3D) surface reconstruction, high-resolution (HR) MR image acquisition is needed. However, HR image acquisition is hindered by hardware limitations that result in long acquisition time and low spatial coverage. Single image super-resolution (SISR) can alleviate these problems by converting a low-resolution (LR) image to an HR image. However, unlike 2D SISR methods, conventional 3D methods incur a large computational cost and require abundant data. Further, 3D boundaries for surface reconstruction based on MR images have not been sufficiently investigated. We herein propose a cost-efficient novel regression-based framework for super-resolution of 3D brain MRI that directly analyzes 3D features by introducing a tensor using gradient information. We initially cluster features using tensors to create labels for both the training and testing stages. In the training stage, for each label, we collect LR patches and corresponding HR intensities to compute filters. In the testing stage, for each voxel, we construct a tensor to obtain a feature and predict the HR intensity using trained filters. We also propose a patch span reduction method by limiting patch orientation to reduce the orientation span and increase shape variety. Using only 30 masked T1-weighted brain MR volumes from the Human Connectome Project (HCP) 900 dataset, the proposed algorithm exhibited superior performance in terms of HR boundary recovery in the cerebral cortex as well as improved overall quality compared to conventional methods.

Background: Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease. Methods: We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC. Results: The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV’s cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p

IntroductionAnalysis of task fMRI studies is typically based on using ordinary least squares within a voxel- or vertex-wise linear regression framework known as the general linear model. This use produces estimates and standard errors of the regression coefficients representing amplitudes of task-induced activations. To produce valid statistical inferences, several key statistical assumptions must be met, including that of independent residuals. Since task fMRI residuals often exhibit temporal autocorrelation, it is common practice to perform “prewhitening” to mitigate that dependence. Prewhitening involves estimating the residual correlation structure and then applying a filter to induce residual temporal independence. While theoretically straightforward, a major challenge in prewhitening for fMRI data is accurately estimating the residual autocorrelation at each voxel or vertex of the brain. Assuming a global model for autocorrelation, which is the default in several standard fMRI software tools, may under- or over-whiten in certain areas and produce differential false positive control across the brain. The increasing popularity of multiband acquisitions with faster temporal resolution increases the challenge of effective prewhitening because more complex models are required to accurately capture the strength and structure of autocorrelation. These issues are becoming more critical now because of a trend toward subject-level analysis and inference. In group-average or group-difference analyses, the within-subject residual correlation structure is accounted for implicitly, so inadequate prewhitening is of little real consequence. For individual subject inference, however, accurate prewhitening is crucial to avoid inflated or spatially variable false positive rates.MethodsIn this paper, we first thoroughly examine the patterns, sources and strength of residual autocorrelation in multiband task fMRI data. Second, we evaluate the ability of different autoregressive (AR) model-based prewhitening strategies to effectively mitigate autocorrelation and control false positives. We consider two main factors: the choice of AR model order and the level of spatial regularization of AR model coefficients, ranging from local smoothing to global averaging. We also consider determining the AR model order optimally at every vertex, but we do not observe an additional benefit of this over the use of higher-order AR models (e.g. (AR(6)). To overcome the computational challenge associated with spatially variable prewhitening, we developed a computationally efficient R implementation using parallelization and fast C++ backend code. This implementation is included in the open source R package BayesfMRI.ResultsWe find that residual autocorrelation exhibits marked spatial variance across the cortex and is influenced by many factors including the task being performed, the specific acquisition protocol, mis-modeling of the hemodynamic response function, unmodeled noise due to subject head motion, and systematic individual differences. We also find that local regularization is much more effective than global averaging at mitigating autocorrelation. While increasing the AR model order is also helpful, it has a lesser effect than allowing AR coefficients to vary spatially. We find that prewhitening with an AR(6) model with local regularization is effective at reducing or even eliminating autocorrelation and controlling false positives.ConclusionOur analysis revealed dramatic spatial differences in autocorrelation across the cortex. This spatial topology is unique to each session, being influenced by the task being performed, the acquisition technique, various modeling choices, and individual differences. If not accounted for, these differences will result in differential false positive control and power across the cortex and across subjects.

Abstract Background Novel neuroimaging strategies have the potential to offer new insights into the mechanistic basis for trigeminal neuralgia (TN). The present study aims to conduct whole-brain morphometry analyses of TN patients and to assess the value of group-level neocortical and subcortical structural patterns as tools for diagnostic biomarker exploration. Methods Cortical thickness, surface area, and myelin levels in the neocortex were measured via magnetic resonance imaging (MRI). The radial distance and the Jacobian determinant of the subcortex in 43 TN patients and 43 matched controls were compared. Pattern learning algorithms were employed to establish the utility of group-level MRI findings as tools for predicting TN. An additional 40 control patients with hemifacial spasms were then evaluated to assess algorithm sensitivity and specificity. Results TN patients exhibited reductions in cortical indices in the anterior cingulate cortex (ACC), the midcingulate cortex (MCC), and the posterior cingulate cortex (PCC) relative to controls. They further presented with widespread subcortical volume reduction that was most evident in the putamen, the thalamus, the accumbens, the pallidum, and the hippocampus. Whole brain-level morphological alterations successfully enable automated TN diagnosis with high specificity (TN: 95.35 %; disease controls: 46.51 %). Conclusions TN is associated with a distinctive whole-brain structural neuroimaging pattern, underscoring the value of machine learning as an approach to differentiating between morphological phenotypes, ultimately revealing the full spectrum of this disease and highlighting relevant diagnostic biomarkers.

This study aimed to investigate abnormal tinnitus activity by evaluating brain surface-based amplitude of low-frequency fluctuation (ALFF) changes detected by resting-state functional magnetic resonance imaging (RS-fMRI) in patients with idiopathic tinnitus before and after 24 weeks of sound therapy. We hypothesized that sound therapy could gradually return cortical local brain function to a relatively normal range. In this prospective observational study, we recruited thirty-three tinnitus patients who had undergone 24 weeks of sound therapy and 26 matched healthy controls (HCs). For the two groups of subjects, we analyzed the spontaneous neural activity of tinnitus patients by cortical ALFF and detected its correlation with clinical indicators of tinnitus. Patients' Tinnitus Handicap Inventory (THI) scores were assessed to determine the severity of their tinnitus before and after treatment. Two-way mixed model analysis of variance and Pearson's correlation analysis were used in the statistical analysis. Student–Newman–Keuls tests were used in the post hoc analysis. Interaction effects between the two groups and between the two scans revealing local neural activity as assessed by ALFF were observed in the bilateral dorsal stream visual cortex (DSVC), bilateral posterior cingulate cortex (PCC), bilateral anterior cingulate and medial prefrontal cortex (ACC and MPC), left temporo-parieto-occipital junction (TPOJ), left orbital and polar frontal cortex (OPFC), left paracentral lobular and mid cingulate cortex (PCL and MCC), right insular and frontal opercular cortex (IFOC), and left early visual cortex (EVC). Importantly, local functional activity in the left TPOJ and right PCC in the patient group was significantly lower than that in the HCs at baseline and was increased to relatively normal levels after treatment. The 24-week sound therapy tinnitus group demonstrated significantly higher ALFF in the left TPOJ and right PCC than in the tinnitus baseline group. Also, compared with the HC baseline group and the 24-week HC group, the 24-week sound therapy tinnitus group demonstrated slightly lower or higher ALFF in the left TPOJ and right PCC, and there were no differences between the 24-week sound therapy tinnitus and HC groups. Decreased THI scores and ALFF changes in the abovementioned brain regions were not correlated. Taken together, surface-based RS-fMRI can provide more subtle local functional activity to explain the mechanism of tinnitus treatment, and long-term sound therapy had a normalizing effect on tinnitus patients. [ABSTRACT FROM AUTHOR]

This study aimed to investigate abnormal tinnitus activity by evaluating brain surface-based amplitude of low-frequency fluctuation (ALFF) changes detected by resting-state functional magnetic resonance imaging (RS-fMRI) in patients with idiopathic tinnitus before and after 24 weeks of sound therapy. We hypothesized that sound therapy could gradually return cortical local brain function to a relatively normal range. In this prospective observational study, we recruited thirty-three tinnitus patients who had undergone 24 weeks of sound therapy and 26 matched healthy controls (HCs). For the two groups of subjects, we analyzed the spontaneous neural activity of tinnitus patients by cortical ALFF and detected its correlation with clinical indicators of tinnitus. Patients’ Tinnitus Handicap Inventory (THI) scores were assessed to determine the severity of their tinnitus before and after treatment. Two-way mixed model analysis of variance and Pearson’s correlation analysis were used in the statistical analysis. Student–Newman–Keuls tests were used in the post hoc analysis. Interaction effects between the two groups and between the two scans revealing local neural activity as assessed by ALFF were observed in the bilateral dorsal stream visual cortex (DSVC), bilateral posterior cingulate cortex (PCC), bilateral anterior cingulate and medial prefrontal cortex (ACC and MPC), left temporo-parieto-occipital junction (TPOJ), left orbital and polar frontal cortex (OPFC), left paracentral lobular and mid cingulate cortex (PCL and MCC), right insular and frontal opercular cortex (IFOC), and left early visual cortex (EVC). Importantly, local functional activity in the left TPOJ and right PCC in the patient group was significantly lower than that in the HCs at baseline and was increased to relatively normal levels after treatment. The 24-week sound therapy tinnitus group demonstrated significantly higher ALFF in the left TPOJ and right PCC than in the tinnitus baseline group. Also, compared with the HC baseline group and the 24-week HC group, the 24-week sound therapy tinnitus group demonstrated slightly lower or higher ALFF in the left TPOJ and right PCC, and there were no differences between the 24-week sound therapy tinnitus and HC groups. Decreased THI scores and ALFF changes in the abovementioned brain regions were not correlated. Taken together, surface-based RS-fMRI can provide more subtle local functional activity to explain the mechanism of tinnitus treatment, and long-term sound therapy had a normalizing effect on tinnitus patients.

Background: Before the apparent cognitive decline, subjects on the course of Alzheimer's disease (AD) can have significantly altered spontaneous brain activity, which could be potentially used for early diagnosis. As previous studies investigating local brain activity may suffer from the problem of cortical signal aliasing during volume-based analysis, we aimed to investigate the cortical functional alterations in the AD continuum using a surface-based approach. Methods: Based on biomarker profile "A/T," we included 11 healthy controls (HC, A–T–), 22 preclinical AD (CU, A+T+), 33 prodromal AD (MCI, A+T+), and 20 AD with dementia (d-AD, A+T+) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The amplitude of low-frequency fluctuation (ALFF) method was used to evaluate the changes of spontaneous brain activity, which was performed in the classic frequency band (0.01–0.08 Hz), slow-4 (0.027–0.073 Hz) band, and slow-5 (0.01–0.027 Hz) band. Results: Under classic frequency band and slow-4 band, analysis of covariance (ANCOVA) showed that there were significant differences of standardized ALFF (zALFF) in the left posterior cingulate cortex (PCC) among the four groups. The post-hoc analyses showed that under the classic frequency band, the AD group had significantly decreased zALFF compared with the other three groups, and the cognitively unimpaired (CU) group had decreased zALFF compared with the healthy control (HC) group. Under the slow-4 band, more group differences were detected (HC > CU/MCI > d-AD). The accuracy of classifying CU, mild cognitive impairment (MCI), and AD from HC by left PCC activity under the slow-4 band were 0.774, 0.744, and 0.920, respectively. Moreover, the zALFF values of the left PCC had significant correlations with cerebrospinal fluid (CSF) biomarkers and neuropsychological tests. Conclusions: Spontaneous brain activity in the left PCC may decrease in preclinical AD when cognitive functions were relatively normal. The combination of a surfaced-based approach and specific frequency band analysis may increase sensitivity for the identification of preclinical AD subjects. [ABSTRACT FROM AUTHOR]

Background: Novel neuroimaging strategies have the potential to offer new insights into the mechanistic basis for trigeminal neuralgia (TN). The present study aims to conduct whole-brain morphometry analyses of TN patients and to assess the value of group-level neocortical and subcortical structural patterns as tools for diagnostic biomarker exploration. Methods: Cortical thickness, surface area, and myelin levels in the neocortex were measured via magnetic resonance imaging (MRI). The radial distance and the Jacobian determinant of the subcortex in 43 TN patients and 43 matched controls were compared. Pattern learning algorithms were employed to establish the utility of group-level MRI findings as tools for predicting TN. An additional 40 control patients with hemifacial spasms were then evaluated to assess algorithm sensitivity and specificity. Results: TN patients exhibited reductions in cortical indices in the anterior cingulate cortex (ACC), the midcingulate cortex (MCC), and the posterior cingulate cortex (PCC) relative to controls. They further presented with widespread subcortical volume reduction that was most evident in the putamen, the thalamus, the accumbens, the pallidum, and the hippocampus. Whole brain-level morphological alterations successfully enable automated TN diagnosis with high specificity (TN: 95.35 %; disease controls: 46.51 %). Conclusions: TN is associated with a distinctive whole-brain structural neuroimaging pattern, underscoring the value of machine learning as an approach to differentiating between morphological phenotypes, ultimately revealing the full spectrum of this disease and highlighting relevant diagnostic biomarkers. [ABSTRACT FROM AUTHOR]

Methods for imaging of cerebral blood flow do not typically resolve the cortex and thus underestimate flow. However, recent work with high-resolution MRI has emphasized the regional and depth-dependent structural, functional and relaxation times variations within the cortex. Using high-resolution Arterial Spin Labeling (ASL) and T1 mapping acquisitions, we sought to probe the effects of spatial resolution and tissue heterogeneity on cortical cerebral blood flow (CBF) measurements with ASL. We acquired high-resolution (1.6mm) 3 whole brain ASL data in a cohort of 10 volunteers at 3T, along with T1 and transit-time (ATT) mapping, followed by group cortical surface-based analysis using FreeSurfer of the different measured parameters. Fully resolved regional analysis showed higher than average mid-thickness CBF in primary motor areas (+15%,p<0.002), frontal regions (+17%,p<0.01) and auditory cortex, while occipital regions had lower average CBF (-20%,p<10−5). ASL signal was higher towards the pial surface but correction for the shorter T1 near the white matter surface reverses this gradient, at least when using the low-resolution ATT map. Similar to structural measures, fully-resolved ASL CBF measures show significant differences across cortical regions. Depth-dependent variation of T1 in the cortex complicates interpretation of depth-dependent ASL signal and may have implications for the accurate CBF quantification at lower resolutions. [ABSTRACT FROM AUTHOR]

To extract Diffusion Tensor Imaging (DTI) parameters from the human cortex, the inner and outer boundaries of the cortex are usually defined on 3D-T1-weighted images and then applied to the co-registered DTI. However, this analysis requires the acquisition of an additional high-resolution structural image that may not be practical in various imaging studies. Here an automatic cortical boundary segmentation method was developed to work directly only on the native DTI images by using fractional anisotropy (FA) maps and mean diffusion weighted images (DWI), the latter with acceptable gray-white matter image contrast. This new method was compared to the conventional cortical segmentations generated from high-resolution T1 structural images in 5 participants. In addition, the proposed method was applied to 15 healthy young adults (10 cross-sectional, 5 test-retest) to measure FA, MD, and radiality of the primary eigenvector across the cortex on whole-brain 1.5 mm isotropic images acquired in 3.5 min at 3T. The proposed method generated reasonable segmentations of the cortical boundaries for all individuals and large proportions of the proposed method segmentations (more than 85%) were within ±1 mm from those generated with the conventional approach on higher resolution T1 structural images. Both FA (~0.15) and MD (~0.77 × 10−3 mm2/s) extracted halfway between the cortical boundaries were relatively stable across the cortex, although focal regions such as the posterior bank of the central sulcus, anterior insula, and medial temporal lobe showed higher FA. The primary eigenvectors were primarily oriented radially to the middle cortical surface, but there were tangential orientations in the sulcal fundi as well as in the posterior bank of the central sulcus. The proposed method demonstrates the feasibility and accuracy of cortical analysis in native DTI space while avoiding the acquisition of other imaging contrasts like 3D T1-weighted scans.

Background: Before the apparent cognitive decline, subjects on the course of Alzheimer's disease (AD) can have significantly altered spontaneous brain activity, which could be potentially used for early diagnosis. As previous studies investigating local brain activity may suffer from the problem of cortical signal aliasing during volume-based analysis, we aimed to investigate the cortical functional alterations in the AD continuum using a surface-based approach.Methods: Based on biomarker profile “A/T,” we included 11 healthy controls (HC, A–T–), 22 preclinical AD (CU, A+T+), 33 prodromal AD (MCI, A+T+), and 20 AD with dementia (d-AD, A+T+) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The amplitude of low-frequency fluctuation (ALFF) method was used to evaluate the changes of spontaneous brain activity, which was performed in the classic frequency band (0.01–0.08 Hz), slow-4 (0.027–0.073 Hz) band, and slow-5 (0.01–0.027 Hz) band.Results: Under classic frequency band and slow-4 band, analysis of covariance (ANCOVA) showed that there were significant differences of standardized ALFF (zALFF) in the left posterior cingulate cortex (PCC) among the four groups. The post-hoc analyses showed that under the classic frequency band, the AD group had significantly decreased zALFF compared with the other three groups, and the cognitively unimpaired (CU) group had decreased zALFF compared with the healthy control (HC) group. Under the slow-4 band, more group differences were detected (HC > CU/MCI > d-AD). The accuracy of classifying CU, mild cognitive impairment (MCI), and AD from HC by left PCC activity under the slow-4 band were 0.774, 0.744, and 0.920, respectively. Moreover, the zALFF values of the left PCC had significant correlations with cerebrospinal fluid (CSF) biomarkers and neuropsychological tests.Conclusions: Spontaneous brain activity in the left PCC may decrease in preclinical AD when cognitive functions were relatively normal. The combination of a surfaced-based approach and specific frequency band analysis may increase sensitivity for the identification of preclinical AD subjects.

Working memory training (WMT) has been shown to have effects on cognitive performance, the precise effects and the underlying neurobiological mechanisms are, however, still a matter of debate. In particular, the impact of WMT on gray matter morphology is still rather unclear. In the present study, 59 healthy middle‐aged participants (age range 50–65 years) were pseudo‐randomly single‐blinded allocated to an 8‐week adaptive WMT or an 8‐week nonadaptive intervention. Before and after the intervention, high resolution magnetic resonance imaging (MRI) was performed and cognitive test performance was assessed in all participants. Vertex‐wise cortical volume, thickness, surface area, and cortical folding was calculated. Seven subcortical volumes of interest and global mean cortical thickness were also measured. Comparisons of symmetrized percent change (SPC) between groups were conducted to identify group by time interactions. Greater increases in cortical gyrification in bilateral parietal regions, including superior parietal cortex and inferior parietal lobule as well as precuneus, greater increases in cortical volume and thickness in bilateral primary motor cortex, and changes in surface area in bilateral occipital cortex (medial and lateral occipital cortex) were detected in WMT group after training compared to active controls. Structural training‐induced changes in WM‐related regions, especially parietal regions, might provide a better brain processing environment for higher WM load. [ABSTRACT FROM AUTHOR]

To quantitatively evaluate the brain MRI morphological abnormalities in patients with cyclin-dependent kinase-like 5 deficiency disorder (CDD) on a group level and longitudinally. We performed surface-based MRI analysis on high-resolution T1-weighted images on three CDD patients scanned at age of three years, and compared with 12 age- and gender-matched healthy controls. We further examined the longitudinal morphological changes in one patient with a follow-up of 5 years. CDD patients presented significant reductions in total intracranial volume, total gray matter (GM) volume and subcortical GM volume compared to controls. For subcortical regions, significant GM volume reductions were seen in the brain stem, bilateral thalamus, bilateral hippocampus, bilateral cerebellum and left amygdala. Although GM volume of cortical mantle did not show statistical differences overall, significant reduction was detected in bilateral parietal, left occipital and right temporal lobes. Cortical thickness exhibited significant decreases in bilateral occipital, parietal and temporal lobes, while surface area did not show any significant differences. Longitudinal follow-up in one patient revealed a monotonic downward trend of relative volume in the majority of brain regions. The relative surface area appeared to gain age-related growth, whereas the relative cortical thickness exhibited a striking progressive decline over time. Quantitative morphology analysis in children with CDD showed global volume loss in the cortex and more notably in the subcortical gray matter, with a progressive trend along with the disease course. Cortical thickness is a more sensitive measure to disclose cortical atrophy and disease progression than surface area. • Quantitative MRI morphological abnormalities were first reported in children with CDD. • Children with CDD showed global volume loss in cortex and more notably in subcortical GM. • Longitudinal follow-up showed monotonic downward trend of relative GM volume along with disease course. • Cortical thickness was more sensitive to disclose GM atrophy and disease progression than surface area. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI) and Alzheimer's disease (AD) are associated with a progressive loss of cognitive abilities. In the present report, we assessed the relationship of memory and executive function with brain structure in a sample of 810 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, including 188 AD, 396 MCI, and 226 healthy older adults (HC). Composite scores of memory (ADNI-Mem) and executive function (ADNI-Exec) were generated by applying modern psychometric theory to item-level data from ADNI's neuropsychological battery. We performed voxel-based morphometry (VBM) and surface-based association (SurfStat) analyses to evaluate relationships of ADNI-Mem and ADNI-Exec with grey matter (GM) density and cortical thickness across the whole brain in the combined sample and within diagnostic groups. We observed strong associations between ADNI-Mem and medial and lateral temporal lobe atrophy. Lower ADNI-Exec scores were associated with advanced GM and cortical atrophy across broadly distributed regions, most impressively in the bilateral parietal and temporal lobes. We also evaluated ADNI-Exec adjusted for ADNI-Mem, and found associations with GM density and cortical thickness primarily in the bilateral parietal, temporal, and frontal lobes. Within-group analyses suggest these associations are strongest in patients with MCI and AD. The present study provides insight into the spatially unbiased associations between brain atrophy and memory and executive function, and underscores the importance of structural brain changes in early cognitive decline.

Abstract Continued improvement in MRI acquisition technology has made functional MRI (fMRI) with small isotropic voxel sizes down to 1 mm and below more commonly available. Although many conventional fMRI studies seek to investigate regional patterns of cortical activation for which conventional voxel sizes of 3 mm and larger provide sufficient spatial resolution, smaller voxels can help avoid contamination from adjacent white matter (WM) and cerebrospinal fluid (CSF), and thereby increase the specificity of fMRI to signal changes within the gray matter. Unfortunately, temporal signal-to-noise ratio (tSNR), a metric of fMRI sensitivity, is reduced in high-resolution acquisitions, which offsets the benefits of small voxels. Here we introduce a framework that combines small, isotropic fMRI voxels acquired at 7 T field strength with a novel anatomically-informed, surface mesh-navigated spatial smoothing that can provide both higher detection power and higher resolution than conventional voxel sizes. Our smoothing approach uses a family of intracortical surface meshes and allows for kernels of various shapes and sizes, including curved 3D kernels that adapt to and track the cortical folding pattern. Our goal is to restrict smoothing to the cortical gray matter ribbon and avoid noise contamination from CSF and signal dilution from WM via partial volume effects. We found that the intracortical kernel that maximizes tSNR does not maximize percent signal change (ΔS/S), and therefore the kernel configuration that optimizes detection power cannot be determined from tSNR considerations alone. However, several kernel configurations provided a favorable balance between boosting tSNR and ΔS/S, and allowed a 1.1-mm isotropic fMRI acquisition to have higher performance after smoothing (in terms of both detection power and spatial resolution) compared to an unsmoothed 3.0-mm isotropic fMRI acquisition. Overall, the results of this study support the strategy of acquiring voxels smaller than the cortical thickness, even for studies not requiring high spatial resolution, and smoothing them down within the cortical ribbon with a kernel of an appropriate shape to achieve the best performance—thus decoupling the choice of fMRI voxel size from the spatial resolution requirements of the particular study. The improvement of this new intracortical smoothing approach over conventional surface-based smoothing is expected to be modest for conventional resolutions, however the improvement is expected to increase with higher resolutions. This framework can also be applied to anatomically-informed intracortical smoothing of higher-resolution data (e.g. along columns and layers) in studies with prior information about the spatial structure of activation. Highlights • We introduce an anatomically-informed intracortical spatial smoothing method. • Smoothing fMRI data while avoiding white matter and CSF has advantages. • Different smoothing kernels maximize SNR and percent signal change. • Smoothing small-voxel data can provide improved detection and comparable resolution. • This smoothing framework can also benefit laminar and columnar activation patterns. [ABSTRACT FROM AUTHOR]

Objectives: Although neuroanatomical abnormalities in subjects at clinical high risk for psychosis have been considered a putative biomarker of psychosis, relevance of cortical thickness alterations remains contested due to discrepant findings. Inconsistencies persist in Asian clinical high risk studies, despite their advantageous settings well-controlled for confounds. Attributes of cortical thickness alterations in clinical high risk subjects warrant further examination. Methods: We examined cortical thickness at the whole-brain level in 74 clinical high risk subjects and 34 demographically matched healthy controls recruited from Seoul Youth Clinic, South Korea. Clinical symptoms were assessed using the Scale of Prodromal Symptoms, and their associations with cortical thickness were explored using partial correlation analysis. Results: Compared to healthy control, clinical high risk exhibited significant cortical thinning in bilateral prefrontal cortex and inferior parietal lobule clusters. Reduced thickness in the left prefrontal cortex cluster was associated with more severe Scale of Prodromal Symptoms general symptoms scores and the right inferior parietal lobule cluster with Scale of Prodromal Symptoms disorganization symptoms. Conclusions: Thickness deficits found in the present clinical high risk sample demonstrated a degree of consistency with those reported in the previous Seoul Youth Clinic study. While inconsistencies reported between the present and previous Seoul Youth Clinic samples may reflect markedly decreased rate of converters, consistencies may be relevant to clinical attributes beyond transition, such as the prevalence of comorbidities. Particular recruitment strategies employed for sample selections should also be considered for findings in Asian clinical high risk samples. Our results suggest potential utility of cortical thickness alterations in clinical high risk subjects beyond the frame of transition. [ABSTRACT FROM AUTHOR]

In machine learning, one of the most popular deep learning methods is the convolutional neural network (CNN), which utilizes shared local filters and hierarchical information processing analogous to the brain’s visual system. Despite its popularity in recognizing two-dimensional (2D) images, the conventional CNN is not directly applicable to semi-regular geometric mesh surfaces, on which the cerebral cortex is often represented. In order to apply the CNN to surface-based brain research, we propose a geometric CNN (gCNN) that deals with data representation on a mesh surface and renders pattern recognition in a multi-shell mesh structure. To make it compatible with the conventional CNN toolbox, the gCNN includes data sampling over the surface, and a data reshaping method for the convolution and pooling layers. We evaluated the performance of the gCNN in sex classification using cortical thickness maps of both hemispheres from the Human Connectome Project (HCP). The classification accuracy of the gCNN was significantly higher than those of a support vector machine (SVM) and a 2D CNN for thickness maps generated by a map projection. The gCNN also demonstrated position invariance of local features, which rendered reuse of its pre-trained model for applications other than that for which the model was trained without significant distortion in the final outcome. The superior performance of the gCNN is attributable to CNN properties stemming from its brain-like architecture, and its surface-based representation of cortical information. The gCNN provides much-needed access to surface-based machine learning, which can be used in both scientific investigations and clinical applications.

Background: Functional constipation (FCon) is a common functional gastrointestinal disorder (FGID). Studies have indicated a higher likelihood of psychiatric disorders, such as anxiety, depression, sleep disturbances, and impaired concentration, among patients with FCon. However, the underlying pathophysiological mechanisms responsible for these symptoms in FCon patients remain to be fully elucidated. The human brain is a complex network architecture with several fundamental organizational properties. Neurological interactions between gut symptoms and psychiatric issues may be closely associated with these complex networks., Methods: In the present study, a total of 35 patients with FCon and 40 healthy controls (HC) were recruited for a series of clinical examinations and resting-state functional magnetic imaging (RS-fMRI). We employed the surface-based analysis (SBA) approach, utilizing the Schaefer cortical parcellation template and Tikhonov regularization. Graph theoretical analysis (GTA) and functional connectivity (FC) analysis of RS-fMRI were conducted to investigate the aberrant network alterations between the two groups. Additionally, correlation analyses were performed between the network indices and clinical variables in patients with FCon., Results: At the global level, we found altered topological properties and networks in patients with FCon, mainly including the significantly increased clustering coefficient (C P ), local efficiency (E loc ), and shortest path length (L P ), whereas the decreased global efficiency (E glob ) compared to HC. At the regional level, patients with FCon exhibited increased nodal efficiency in the frontoparietal network (FPN). Furthermore, FC analysis demonstrated several functional alterations within and between the Yeo 7 networks, particularly including visual network (VN), limbic network (LN), default mode network (DMN), and somatosensory-motor network (SMN) in sub-network and large-scale network analysis. Correlation analysis revealed that there were no significant associations between the network metrics and clinical variables in the present study., Conclusion: These results highlight the altered topological architecture of functional brain networks associated with visual perception abilities, emotion regulation, sensorimotor processing, and attentional control, which may contribute to effectively targeted treatment modalities for patients with FCon., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Yu, Yu, Li, Cong, Wang, Fan, Wang, Zhou, Xu, Li and Liu.)

In machine learning, one of the most popular deep learning methods is the convolutional neural network (CNN), which utilizes shared local filters and hierarchical information processing analogous to the brain’s visual system. Despite its popularity in recognizing two-dimensional (2D) images, the conventional CNN is not directly applicable to semi-regular geometric mesh surfaces, on which the cerebral cortex is often represented. In order to apply the CNN to surface-based brain research, we propose a geometric CNN (gCNN) that deals with data representation on a mesh surface and renders pattern recognition in a multi-shell mesh structure. To make it compatible with the conventional CNN toolbox, the gCNN includes data sampling over the surface, and a data reshaping method for the convolution and pooling layers. We evaluated the performance of the gCNN in sex classification using cortical thickness maps of both hemispheres from the Human Connectome Project (HCP). The classification accuracy of the gCNN was significantly higher than those of a support vector machine (SVM) and a 2D CNN for thickness maps generated by a map projection. The gCNN also demonstrated position invariance of local features, which rendered reuse of its pre-trained model for applications other than that for which the model was trained without significant distortion in the final outcome. The superior performance of the gCNN is attributable to CNN properties stemming from its brain-like architecture, and its surface-based representation of cortical information. The gCNN provides much-needed access to surface-based machine learning, which can be used in both scientific investigations and clinical applications. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this study was to investigate the interplay between structural connectivity and cortical demyelination in early multiple sclerosis. About 27 multiple sclerosis patients and 18 age‐matched controls underwent two MRI scanning sessions. The first was done at 7T and involved acquiring quantitative T1 and T2* high‐resolution maps to estimate cortical myelination. The second was done on a Connectom scanner and consisted of acquiring high angular resolution diffusion‐weighted images to compute white matter structural connectivity metrics: strength, clustering and local efficiency. To further investigate the interplay between structural connectivity and cortical demyelination, patients were divided into four groups according to disease‐duration: 0–1 year, 1–2 years, 2–3 years, and >3 years. ANOVA and Spearman's correlations were used to highlight relations between metrics. ANOVA detected a significant effect between disease duration and both cortical myelin (p = 2 × 10−8) and connectivity metrics (p < 10−4). We observed significant cortical myelin loss in the shorter disease‐duration cohorts (0–1 year, p = .0015), and an increase in connectivity in the longer disease‐duration cohort (2–3 years, strength: p = .01, local efficiency: p = .002, clustering: p = .001). Moreover, significant covariations between myelin estimation and white matter connectivity metrics were observed: Spearman's Rho correlation coefficients of 0.52 (p = .0003), 0.55 (p = .0001), and 0.53 (p = .0001) for strength, local efficiency, and clustering, respectively. An association between cortical myelin loss and changes in white matter connectivity in early multiple sclerosis was detected. These changes in network organization might be the result of compensatory mechanisms in response to the ongoing cortical diffuse damage in the early stages of multiple sclerosis. [ABSTRACT FROM AUTHOR]

Functional MRI (fMRI) benefits from both increased sensitivity and specificity with increasing magnetic field strength, making it a key application for Ultra-High Field (UHF) MRI scanners. Most UHF-fMRI studies utilize the dramatic increases in sensitivity and specificity to acquire high-resolution data reaching sub-millimeter scales, which enable new classes of experiments to probe the functional organization of the human brain. This review article surveys advanced data analysis strategies developed for high-resolution fMRI at UHF. These include strategies designed to mitigate distortion and artifacts associated with higher fields in ways that attempt to preserve spatial resolution of the fMRI data, as well as recently introduced analysis techniques that are enabled by these extremely high-resolution data. Particular focus is placed on anatomically-informed analyses, including cortical surface-based analysis, which are powerful techniques that can guide each step of the analysis from preprocessing to statistical analysis to interpretation and visualization. New intracortical analysis techniques for laminar and columnar fMRI are also reviewed and discussed. Prospects for single-subject individualized analyses are also presented and discussed. Altogether, there are both specific challenges and opportunities presented by UHF-fMRI, and the use of proper analysis strategies can help these valuable data reach their full potential. [ABSTRACT FROM AUTHOR]

APOE is one of the strongest genetic factors associated with information processing speed (IPS). Herein, we explored the neural substrates underlying APOE-related IPS alteration by measuring lobar distribution of white matter hyperintensities (WMH), cortical grey matter volume (GMV) and thickness. Using the ADNI database, we evaluated 178 cognitively normal elderly individuals including 34 APOE ε2 carriers, 54 APOE ε4 carriers and 90 ε3 homozygotes. IPS was determined using Trail Making Tests (TMT). We quantified lobar distribution of WMH, cortical GM lobar volume, cortical thickness among three groups. Finally, we used Pearson's correlation and general linear models to examine structural MRI markers in relation to IPS. There were significant differences of IPS among groups, with ε4 carriers displaying the worst performance. Across groups, significant differences in frontal and parietal WMH load were observed (the highest in ε4 carriers); however, no significant differences in cortical GMV and thickness were found. Pearson's correlation analysis showed parietal WMH volume was significantly related with IPS, especially in ε4 carriers. Subsequently a general linear model demonstrated that parietal WMH volume, age and the interaction between parietal WMH volume and age, was significantly associated with IPS, even after adjusting total intracranial volume (TIV), gender and vascular risk factors. Disruption of WM structure, rather than atrophy of GM, plays a more critical role in APOE ε4 allele-specific IPS. Moreover, specific WMH loci are closely associated with IPS; increased parietal WMH volume, especially in ε4 carriers, was independently contributed to slower IPS. [ABSTRACT FROM AUTHOR]