Diffusion Tensor Imaging Measures Research Articles

Cyto/myeloarchitecture of cortical gray matter and superficial white matter in early neurodevelopment: multimodal MRI study in preterm neonates.

The cerebral cortex undergoes rapid microstructural changes throughout the third trimester. Recently, there has been growing interest on imaging features that represent cyto/myeloarchitecture underlying intracortical myelination, cortical gray matter (GM), and its adjacent superficial whitematter (sWM). Using 92 magnetic resonance imaging scans from 78 preterm neonates, the current study used combined T1-weighted/T2-weighted (T1w/T2w) intensity ratio and diffusion tensor imaging (DTI) measurements, including fractional anisotropy (FA) and mean diffusivity (MD), to characterize the developing cyto/myeloarchitectural architecture. DTI metrics showed a linear trajectory: FA decreased in GM but increased in sWM with time; and MD decreased in both GM and sWM. Conversely, T1w/T2w measurements showed a distinctive parabolic trajectory, revealing additional cyto/myeloarchitectural signature inferred. Furthermore, the spatiotemporal courses were regionally heterogeneous: central, ventral, and temporal regions of GM and sWM exhibited faster T1w/T2w changes; anterior sWM areas exhibited faster FA increases; and central and cingulate areas in GM and sWM exhibited faster MD decreases. These results may explain cyto/myeloarchitectural processes, including dendritic arborization, synaptogenesis, glial proliferation, and radial glial cell organization and apoptosis. Finally, T1w/T2w values were significantly associated with 1-year language and cognitive outcome scores, while MD significantly decreased with intraventricular hemorrhage.

Individual variation in white matter microstructure is related to better recovery from negative stimuli.

The uncinate fasciculus is a white matter tract that may facilitate emotion regulation by carrying connections from the prefrontal cortex to regions of the temporal lobe, including the amygdala. Depression and anxiety are associated with reduced uncinate fasciculus fractional anisotropy (FA)-a diffusion tensor imaging measure related to white matter integrity. In the current study, we tested whether FA in the uncinate fasciculus is associated with individual differences in emotional recovery measured with corrugator supercilii electromyography in response to negative, neutral, and positive images in 108 participants from the Midlife in the US (MIDUS; http://midus.wisc.edu) Refresher study. Corrugator activity is linearly associated with changes in affect, and differentiated negative, neutral, and positive emotional responses. Higher uncinate fasciculus FA was associated with lower corrugator activity 4-8 seconds after negative image offset, indicative of better recovery from negative provocation. In an exploratory analysis, we found a similar association for the inferior fronto-occipital, inferior longitudinal and superior longitudinal fasciculi. These results suggest that the microstructural features of the uncinate fasciculus, and these other association white matter fibers, may support emotion regulatory processes with greater white matter integrity facilitating healthier affective functioning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Psychopathology and Integrity of the Superior Longitudinal Fasciculus in Deficit and Nondeficit Schizophrenia.

The superior longitudinal fasciculus (SLF) is a white matter bundle that connects the frontal areas with the parietal areas. As part of the visuospatial attentional network, it may be involved in the development of schizophrenia. Deficit syndrome (DS) is characterized by primary and enduring negative symptoms. The present study assessed SLF integrity in DS and nondeficit schizophrenia (NDS) patients and examined possible relationships between it and psychopathology. Twenty-six DS patients, 42 NDS patients, and 36 healthy controls (HC) underwent psychiatric evaluation and diffusion tensor imaging (DTI). After post-processing, fractional anisotropy (FA) values within the SLF were analyzed. Psychopathology was assessed with the Positive and Negative Syndrome Scale, Brief Negative Symptom Scale, and Self-evaluation of Negative Symptoms. The PANSS proxy for the deficit syndrome was used to diagnose DS. NDS patients had lower FA values than HC. DS patients had greater negative symptoms than NDS patients. After differentiating clinical groups and HC, we found no significant correlations between DTI measures and psychopathological dimensions. These results suggest that changes in SLF integrity are related to schizophrenia, and frontoparietal dysconnection plays a role in its etiopathogenesis. We confirmed that DS patients have greater negative psychopathology than NDS patients. These results are preliminary; further studies are needed.

A dynamic relation between whole-brain white matter microstructural integrity and anxiety symptoms in preadolescent females with pathological anxiety

Pathological anxiety typically emerges during preadolescence and has been linked to alterations in white matter (WM) pathways. Because myelination is critical for efficient neuronal communication, characterizing associations between WM microstructure and symptoms may provide insights into pathophysiological mechanisms associated with childhood pathological anxiety. This longitudinal study examined 182 girls enrolled between the ages of 9–11 that were treatment-naïve at study entry: healthy controls (n = 49), subthreshold-anxiety disorders (AD) (n = 82), or meeting DSM-5 criteria for generalized, social, and/or separation ADs (n = 51), as determined through structured clinical interview. Anxiety severity was assessed with the Clinical Global Impression Scale and Screen for Child Anxiety and Related Emotional Disorders (SCARED). Participants (n = 182) underwent clinical, behavioral, and diffusion tensor imaging (DTI) assessments at study entry, and those with pathological anxiety (subthreshold-AD and AD, n = 133) were followed longitudinally for up to 3 additional years. Cross-sectional ANCOVAs (182 scans) examining control, subthreshold-AD, and AD participants found no significant relations between anxiety and DTI measurements. However, in longitudinal analyses of girls with pathological anxiety (343 scans), linear mixed-effects models demonstrated that increases in anxiety symptoms (SCARED scores) were associated with reductions in whole-brain fractional anisotropy, independent of age (Std. β (95% CI) = −0.06 (−0.09 to −0.03), F(1, 46.24) = 11.90, P = 0.001). Using a longitudinal approach, this study identified a dynamic, within-participant relation between whole-brain WM microstructural integrity and anxiety in girls with pathological anxiety. Given the importance of WM microstructure in modulating neural communication, this finding suggests the possibility that WM development could be a viable target in the treatment of anxiety-related psychopathology.

Multimodal Classification of Alzheimer's Disease and Amnestic Mild Cognitive Impairment: Integrated 18F-FDG PET and DTI Study.

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and memory impairment. Amnestic mild cognitive impairment (aMCI) is the intermediate stage between normal cognitive aging and early dementia caused by AD. It can be challenging to differentiate aMCI patients from healthy controls (HC) and mild AD patients. To validate whether the combination of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) and diffusion tensor imaging (DTI) will improve classification performance compared with that based on a single modality. A total of thirty patients with AD, sixty patients with aMCI, and fifty healthy controls were included. AD was diagnosed according to the National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable. aMCI diagnosis was based on Petersen's criteria. The 18F-FDG PET and DTI measures were each used separately or in combination to evaluate sensitivity, specificity, and accuracy for differentiating HC, aMCI, and AD using receiver operating characteristic analysis together with binary logistic regression. The rate of accuracy was based on the area under the curve (AUC). For classifying AD from HC, we achieve an AUC of 0.96 when combining two modalities of biomarkers and 0.93 when using 18F-FDG PET individually. For classifying aMCI from HC, we achieve an AUC of 0.79 and 0.76 using the best individual modality of biomarkers. Our results show that the combination of two modalities improves classification performance, compared with that using any individual modality.

Characteristics of Brain White Matter Microstructure in HIV Male Patients With Primary Syphilis Co-Infection.

Purpose: To investigate the effect of syphilis infection on the microstructure of white matter (WM) in HIV-infected male patients using diffusion tensor imaging (DTI).Methods: Twenty-seven HIV-infected male patients with current syphilis or a history of syphilis (HIV +/syphilis +), twenty-nine HIV-infected male patients without syphilis co-infection (HIV +/syphilis–), and twenty-nine healthy controls (HC) were enrolled. All participants received DTI, and all patients received comprehensive neuropsychological assessment. Tract-based spatial statistics (TBSS) was adopted to analyze the DTI measures: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Correlation analysis was conducted to investigate the relationships between DTI measures and cognitive performance.Results: There were no significant differences in DTI measures between HIV+/syphilis– and HC. Compared with HC, lower FA was found in body of corpus callosum (BCC), splenium of corpus callosum (SCC), genu of corpus callosum (GCC), the bilateral anterior corona radiata (ACR), superior corona radiata (SCR), posterior corona radiata (PCR), and posterior thalamic radiation (PTR) in HIV+/syphilis+ (p < 0.05). Higher RD was found in BCC and SCC (p < 0.05). Compared with HIV+/syphilis–, lower scores were found in complex motor skills (CMS) in HIV+/syphilis+, lower FA was found in BCC, SCC, GCC, the bilateral ACR, SCR, PCR, PTR, cingulate gyrus (CGC), the right inferior fronto-occipital fasciculus (IFO), the retrolenticular part of internal capsule (RLIC), sagittal stratum (SS), external capsule (EC) in HIV+/syphilis+ (p < 0.01). Correlation analysis uncorrected for multiple comparisons showed there was a positive correlation between FA in GCC and CMS, FA in BCC, and CMS in HIV+/syphilis+.Conclusions: Syphilis co-infection can have an additive or synergistic effect on the brain WM in HIV-infected subjects. HIV-infected patients without syphilis should be actively treated to avoid syphilis infection.

Free-water diffusion tensor imaging detects occult periependymal abnormality in the AQP4-IgG-seropositive neuromyelitis optica spectrum disorder

To compare free-water corrected diffusion tensor imaging (DTI) measures in the normal-appearing periependymal area between AQP4-IgG-seropositive NMOSD and multiple sclerosis (MS) to investigate occult pathophysiology. This prospective study included 44 patients (mean age, 39.52 ± 11.90 years; 14 men) with AQP4-IgG-seropositive NMOSD (n = 20) and MS (n = 24) who underwent DTI between April 2014 and April 2020. Based on free-water corrected DTI measures obtained from normal-appearing periependymal voxels of (1) lateral ventricles and (2) the 3rd and 4th ventricles as dependent variables, MANCOVA was conducted to compare the two groups, using clinical variables as covariates. A significant difference was found between AQP4-IgG-seropositive NMOSD and MS in the 3rd and 4th periependymal voxels (λ = 0.462, P = 0.001). Fractional anisotropy, axial diffusivity was significantly decreased and radial diffusivity was increased in AQP4-IgG-seropositive NMOSD in post-hoc analysis, compared with MS (F = 27.616, P < 0.001, F = 7.336, P = 0.011, and F = 5.800, P = 0.022, respectively). Free-water corrected DTI measures differ in the periependymal area surrounding the diencephalon and brain stem/cerebellum between MS and NMOSD, which may suggest occult white matter injury in areas with distribution of AQP-4 in NMOSD.

Association of Superficial White Matter Alterations with Cerebrospinal Fluid Biomarkers and Cognitive Decline in Neurodegenerative Dementia.

Superficial white matter (SWM) alterations correlated with cognitive decline have been described in Alzheimer's disease (AD). The study aims to extend the investigation of the SWM alterations to AD and non-AD neurodegenerative dementia (ND) and explore the relationship with cerebrospinal fluid (CSF) biomarkers and clinical data. From a database of 323 suspected dementia cases, we retrospectively recruited 55 ND with abnormal amyloid-β42 (AD) and 38 ND with normal amyloid-β42 (non-AD) and collected clinical data, CSF biomarkers, and magnetic resonance images. Ten healthy controls (HC) were recruited for imaging and Mini-Mental State Examination (MMSE). Diffusion tensor imaging (DTI) measurements were performed in the lobar SWM regions and Kruskal Wallis tests were used for among-group comparison. Spearman's correlation tests were performed between DTI measures, CSF biomarkers, and clinical data. AD and non-AD showed significant differences in the DTI measures across the SWM compared to HC. Significant differences between AD and non-AD were detected in the left parietal lobe. DTI measures correlated with amyloid-β42 and MMSE diffusely in the SWM, less extensively with total-tau and phosphorylated tau, and with disease duration in the parietal lobe bilaterally. Widespread SWM alterations occur in both AD and non-AD ND and AD shows appreciably more severe alterations in the parietal SWM. Notably, the alterations in the SWM are strongly linked not only to the cognitive decline but also to the diagnostic CSF biomarkers. Further studies are encouraged to evaluate the DTI measures in the SWM as in vivo non-invasive biomarkers in the preclinical phase.

Causal structure discovery identifies risk factors and early brain markers related to evolution of white matter hyperintensities

Our goal was to understand the complex relationship between age, sex, midlife risk factors, and early white matter changes measured by diffusion tensor imaging (DTI) and their role in the evolution of longitudinal white matter hyperintensities (WMH). We identified 1564 participants (1396 cognitively unimpaired, 151 mild cognitive impairment and 17 dementia participants) with age ranges of 30–90 years from the population-based sample of Mayo Clinic Study of Aging. We used computational causal structure discovery and regression analyses to evaluate the predictors of WMH and DTI, and to ascertain the mediating effect of DTI on WMH. We further derived causal graphs to understand the complex interrelationships between midlife protective factors, vascular risk factors, diffusion changes, and WMH. Older age, female sex, and hypertension were associated with higher baseline and progression of WMH as well as DTI measures (P ≤ 0.003). The effects of hypertension and sex on WMH were partially mediated by microstructural changes measured on DTI. Higher midlife physical activity was predictive of lower WMH through a direct impact on better white matter tract integrity as well as an indirect effect through reducing the risk of hypertension by lowering BMI. This study identified key risks factors, early brain changes, and pathways that may lead to the evolution of WMH.

Differentiating Between Migraine and Post-traumatic Headache Using a Machine Learning Classifier

The objective was to develop classification models differentiating persistent PTH (PPTH) and migraine using clinical data and MRI-based measures of brain structure and functional connectivity. PTH and migraine commonly have similar phenotypes. Furthermore, migraine is a risk factor for developing PTH, sometimes making it difficult to differentiate PTH from exacerbation of migraine symptoms. Thirty-four individuals with migraine without history of TBI and 48 individuals with mild TBI attributed to PPTH but without history of migraine or prior frequent tension type headache were included. Subjects completed questionnaires assessing headache characteristics, mood, sensory hypersensitivities and cognitive function and underwent MRI imaging during the same day. Clinical features and structural brain measures from T1-weighted imaging, diffusion tensor imaging and functional resting-state measures were included as potential variables. A classifier using ridge logistic regression of principal components (PC) was fit. Since PCs can hinder identification of significant variables in a model, a second regression model was fit directly to the data. In the non-PC based model, input variables were selected based on lowest t-test or chi-square p-value by modality. Average accuracy was calculated using leave-one-out cross validation. The importance of variables to the classifier were examined. The PC-based classifier achieved an average classification accuracy of 85%. The non-PC based classifier achieved an average classification accuracy of 74.4%. Both classifiers were more accurate at classifying migraine subjects than PPTH. The PC-based model incorrectly classified 9/48 (18.8%) PPTH subjects compared to 3/34 (8.8%) migraine patients, whereas the non-PC classifier incorrectly classed 16/48 (33.3%) vs 5/34 (14.7%) of migraine subjects. Important variables in the non-PC model included static and dynamic functional connectivity values, several questions from the Beck Depression Inventory, and worsening symptoms and headaches with mental activity. Multivariate models including clinical characteristics, functional connectivity, and brain structural data accurately classify and differentiate PPTH vs migraine.

White matter microstructure associations to amyloid burden in adults with Down syndrome

IntroductionIndividuals with Down syndrome (DS) are at an increased risk of developing Alzheimer’s Disease (AD). One of the early underlying mechanisms in AD pathology is the accumulation of amyloid protein plaques, which are deposited in extracellular gray matter and signify the first stage in the cascade of neurodegenerative events. AD-related neurodegeneration is also evidenced as microstructural changes in white matter. In this work, we explored the correlation of white matter microstructure with amyloid load to assess amyloid-related neurodegeneration in a cohort of adults with DS. MethodsIn this study of 96 adults with DS, the relation of white matter microstructure using diffusion tensor imaging (DTI) and amyloid plaque burden using [11C]PiB PET were examined. The amyloid load (AβL) derived from [11C]PiB was used as a global measure of amyloid burden. AβL and DTI measures were compared using tract-based spatial statistics (TBSS) and corrected for imaging site and chronological age. ResultsTBSS of the DTI maps showed widespread age-by-amyloid interaction with both fractional anisotropy (FA) and mean diffusivity (MD). Further, diffuse negative association of FA and positive association of MD with amyloid were observed. DiscussionThese findings are consistent with the white matter microstructural changes associated with AD disease progression in late onset AD in non-DS populations.

Correlations of diffusion tensor imaging and clinical measures with spinal cord cross-sectional area measurements in pediatric spinal cord injury patients

Purpose: The purpose of this work was to employ a semi-automatic method for measuring spinal cord cross-sectional area (SCCSA) and investigate the correlations between diffusion tensor imaging (DTI) metrics and SCCSA for the cervical and thoracic spinal cord for typically developing pediatric subjects and pediatric subject with spinal cord injury. Methods: Ten typically developing (TD) pediatric subjects and ten pediatric subjects with spinal cord injury (SCI) were imaged using a Siemens Verio 3 T MR scanner to acquire DTI and high-resolution anatomic scans covering the cervical and thoracic spinal cord (C1-T12). SCCSA was measured using a semi-automated edge detection algorithm for the entire spinal cord. DTI metrics were obtained from whole cord axial ROIs at each vertebral level. SCCSA measures were compared to DTI metrics by vertebral level throughout the entire cord, and above and below the injury site. Correlation analysis was performed to compare SCCSA, DTI and clinical measures as determined by the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) examination. Results: In subjects with SCI, FA and SCCSA had a positive correlation (r = 0.81, P < 0.01), while RD and SCCSA had a negative correlation (r = −0.68, P = 0.02) for the full spinal cord. FA and SCCSA were correlated above (r = 0.56, P < 0.01) and below (r = 0.54, P < 0.01) the injury site. TD subjects showed negative correlations between AD and SCCSA (r = −0.73, P = 0.01) and RD and SCCSA (r = −0.79, P < 0.01). Conclusion: The ability to quickly and effectively measure SCCSA in subjects with SCI has the potential to allow for a better understanding of the progression of atrophy following a SCI. Correlations between cord cross section and DTI metrics by vertebral level suggest that imaging inferior and superior to lesion may yield useful information for diagnosis and prognosis.

Associations between mean apparent propagator MRI and cerebrospinal fluid markers of AD pathology, neurodegeneration, and glial activation, in cognitively unimpaired adults

AbstractBackgroundNumerous studies have reported correlations between diffusion tensor imaging (DTI) metrics and cerebrospinal (CSF) biomarkers of Alzheimer’s disease (AD) pathology. However, the assumption of Gaussian diffusion in DTI limits the ability to characterize white matter (WM) microstructural changes. Mean apparent propagator (MAP) MRI is a model that attempts to overcome this limitation by allowing for the estimation of parameters that convey more precise information about WM microstructure.To investigate MAP MRI’s sensitivity to early WM degeneration associated with preclinical, asymptomatic AD pathology, we examined the relationship between CSF biomarkers and MAP MRI microstructural parameters in 92 cognitively unimpaired adults.Method92 cognitively unimpaired controls from the Wisconsin Registry for Alzheimer’s Prevention and the Wisconsin Alzheimer’s Disease Research Center were imaged with multi‐shell diffusion‐weighted MRI. DTI metrics (FA, MD, RD, AxD) were computed and the MAP MRI model was employed to calculate various microstructural parameters: Return to origin probability (RTOP), return to axis probability (RTAP), return to plane probability (RTPP), mean squared displacement (MSD), Non‐Gaussianity (NG), and q‐space inverse variance (QIV). DTI and MAP parameter values were extracted from the cingulum and correlated to Aβ42, P‐Tau, P‐Tau/Aβ42, YKL‐40, and neurofilament light chain (NFL) levels in lumbar cerebrospinal fluid samples.ResultMAP parameters were moderately and significantly correlated to most CSF measures. RTPP was significantly correlated to all 5 CSF measures, while MSD, NG, and QIV were significantly correlated to 4 of 5 CSF measures. NFL, a known marker for axonal degeneration, was significantly related to 5 of the 6 MAP parameters assessed. Meanwhile, correlations between DTI and CSF measures were weak and non‐significant.ConclusionThese preliminary results highlight the potential of MAP MRI to detect early WM deterioration indicative of preclinical, asymptomatic AD and associated neurodegeneration. MAP metrics extracted from a commonly affected WM tract in AD were more strongly and significantly correlated with CSF measures than DTI metrics were, suggesting that MAP MRI may be more useful than DTI for identifying the earliest WM microstructural changes associated with AD. Future work will incorporate longitudinal data and assess the effects of CSF and molecular imaging markers on MAP age trajectories.

Along-tract analysis of the white matter is more informative about brain ageing, compared to whole-tract analysis

Diffusion Tensor Imaging (DTI) enabled the investigation of brain White Matter (WM), both qualitatively to study the macrostructure, and quantitatively to study the microstructure. The quantitative analyses are mostly performed at the whole-tract level, i.e., providing one measure of interest per tract; however, along-tract approaches may provide finer details of the quality of the WM tracts. In this study, using the DWI data collected from 40 young and 40 old individuals, we compared the DTI measures of FA, MD, AD, and RD, estimated by both whole-tract and along-tract approaches in 18 WM bundles, between the two groups. The results of the whole-tract quantitative analysis showed a statistically significant (p-FWER < 0.05) difference between the old and young groups in 6 tracts for FA, 8 tracts for MD, 1 tract for AD, and 7 tracts for RD. On the contrary, the along-tract approach showed differences between the two groups in 10 tracts for FA, 14 tracts for MD, 8 tracts for AD, and 11 tracts for RD. All the differences between the along-tract measures of the two groups had a large effect size (Cohen’d > 0.80). This study showed that the along-tract approach for the analysis of brain WM reveals changes in some WM tracts which had not shown any changes in the whole-tract approach, and therefore this finding emphasizes the utilization of the along-tract approach along with the whole-tract method for a more accurate study of the brain WM.

Can we predict anti-seizure medication response in focal epilepsy using machine learning?

ObjectiveThe aim of this study was to evaluate the feasibility of machine learning approach based on clinical factors and diffusion tensor imaging (DTI) to predict anti-seizure medication (ASM) response in focal epilepsy. We hypothesized that ASM response in focal epilepsy can be predicted using a machine learning approach. MethodsIn this retrospective study conducted at a tertiary hospital, we enrolled 160 patients with newly diagnosed focal epilepsy. All of them underwent DTI from January 2017 to July 2019, with a follow-up at least 12 months after the diagnosis of epilepsy based on regular evaluation of ASMs. We analyzed the patients’ clinical characteristics, and the conventional DTI measurements and extracted the structural connectomic profiles from the DTI. We employed the support vector machine (SVM) algorithm, and a k-fold cross-validation was executed. ResultsThe highest accuracy of classification was ensured based on the clinical factors. A SVM classifier based on the clinical factors revealed an accuracy of 87.5% and an area under curve (AUC) of 0.882. Another SVM classifier based on the conventional DTI measures demonstrated an accuracy of 62.5% and an AUC of 0.611. In addition, an SVM classifier based on the structural connectomic profiles revealed an accuracy of 68.7% and an AUC of 0.667. The AUC of the ROC curve generated from the clinical factors was significantly higher than the ROC curves based on the conventional DTI measures or structural connectomic profiles. ConclusionMachine learning approach is useful in predicting the ASM response in focal epilepsy. The clinical factor is more important than the conventional DTI measures and structural connectomic profiles in predicting the ASM response in focal epilepsy.

The role of multimodal MRI in mild cognitive impairment and Alzheimer's disease.

Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD), where neurodegeneration is not as considerable, thereby potentially increasing the effect of treatments. Therefore, highly sensitive and specific classification of subjects with MCI is necessary, where various MRI modalities have displayed promise. Structural, diffusion, and resting-state (RS) functional MRI analyses were performed on the AD (n = 26), MCI (n = 5), and healthy control (HC) (n = 14) group. Structural analysis was performed via voxel-based morphometry (VBM) and volumetric subcortical segmentation analysis. Fractional anisotropy and mean diffusivity were estimated during the diffusion analysis. RS analysis investigated seed-based functional connectivity. Classification via support vector machine was performed to evaluate which MRI modality most accurately differentiated the groups. Multiple linear regression was conducted to evaluate the MRI modalities correlation with clinical assessment scores. Classification of MCI and HC displayed highest accuracy based on diffusion MRI, which besides demonstrated high correlation with clinical scores. Classification was equally accurate in AD, when using VBM or diffusion tensor imaging measures. Yet, more variance was explained by VBM measures in the clinical assessment scores of the AD group. This study highlights the potential of diffusion MRI in differentiating MCI from HC and AD. However, the results need to be interpreted with caution as sample size and artifacts in the MRI data probably influenced the results.

Intracortical diffusion tensor imaging signature of microstructural changes in frontotemporal lobar degeneration

BackgroundFrontotemporal lobar degeneration (FTLD) is a neuropathological construct with multiple clinical presentations, including the behavioural variant of frontotemporal dementia (bvFTD), primary progressive aphasia—both non-fluent variant (nfvPPA) and semantic variant (svPPA)—progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), characterised by the deposition of abnormal tau protein in the brain. A major challenge for treating FTLD is early diagnosis and accurate discrimination among different syndromes. The main goal here was to investigate the cortical architecture of FTLD syndromes using cortical diffusion tensor imaging (DTI) analysis and to test its power to discriminate between different clinical presentations.MethodsA total of 271 individuals were included in the study: 87 healthy subjects (HS), 31 semantic variant primary progressive aphasia (svPPA), 37 behavioural variant (bvFTD), 30 non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), 47 PSP Richardson’s syndrome (PSP-RS) and 39 CBS cases. 3T MRI T1-weighted images and DTI scans were analysed to extract three cortical DTI derived measures (AngleR, PerpPD and ParlPD) and mean diffusivity (MD), as well as standard volumetric measurements. Whole brain and regional data were extracted. Linear discriminant analysis was used to assess the group discrimination capability of volumetric and DTI measures to differentiate the FTLD syndromes. In addition, in order to further investigate differential diagnosis in CBS and PSP-RS, a subgroup of subjects with autopsy confirmation in the training cohort was used to select features which were then tested in the test cohort.Three different challenges were explored: a binary classification (controls vs all patients), a multiclass classification (HS vs bvFTD vs svPPA vs nfvPPA vs CBS vs PSP-RS) and an additional binary classification to differentiate CBS and PSP-RS using features selected in an autopsy confirmed subcohort.ResultsLinear discriminant analysis revealed that PerpPD was the best feature to distinguish between controls and all patients (ACC 86%). PerpPD regional values were able to classify correctly the different FTLD syndromes with an accuracy of 85.6%. The PerpPD and volumetric values selected to differentiate CBS and PSP-RS patients showed a classification accuracy of 85.2%.Conclusions(I) PerpPD achieved the highest classification power for differentiating healthy controls and FTLD syndromes and FTLD syndromes among themselves. (II) PerpPD regional values could provide an additional marker to differentiate FTD, PSP-RS and CBS.

Association between frontal cortico-limbic white-matter microstructure and risk for pediatric depression

This study aimed to identify white-matter microstructural characteristics associated with risk for pediatric major depressive disorder (MDD) measured by the Child Behavior Checklist (CBCL) Anxiety/Depression scores. Children (N = 32) of both sexes, aged 6–12, underwent T1-weighted whole-head anatomical and diffusion-weighted imaging. Each participant's mean diffusion measure image was generated and thinned to create an alignment-invariant tract representation. Voxel-wise analysis on the resulting map was carried out in Track Based Spatial Statistics (TBSS) using general linear models by regressing the CBCL-Anxiety/Depression score against measures of diffusion tensor imaging (DTI). We also compared these results with prior DTI findings from the same children associated with CBCL-Emotion Dysregulation profile, an indicator for bipolar disorder. TBSS voxel-wise analysis showed a significant negative correlation between fractional anisotropy (FA) and CBCL-Anxiety/Depression scores localized in the right anterior cingulum and connected corpus callosal region. The negative FA correlations in these regions were greater in CBCL-Anxiety/ Depression scores compared to CBCL-Emotional Dysregulation scores. Reduced white-matter connectivity in the anterior cingulum and connected corpus callosal region may represent a biomarker of risk for pediatric MDD. These results may help identify brain differences associated with the development of MDD, and assist with earlier clinical identification of pediatric MDD.

Structural and functional motor-network disruptions predict selective action-concept deficits: Evidence from frontal lobe epilepsy

Built on neurodegenerative lesions models, the disrupted motor grounding hypothesis (DMGH) posits that motor-system alterations selectively impair action comprehension. However, major doubts remain concerning the dissociability, neural signatures, and etiological generalizability of such deficits. Few studies have compared action-concept outcomes between disorders affecting and sparing motor circuitry, and none has examined their multimodal network predictors via data-driven approaches. Here, we first assessed action- and object-concept processing in patients with frontal lobe epilepsy (FLE), patients with posterior cortex epilepsy (PCE), and healthy controls. Then, we examined structural and functional network signatures via diffusion tensor imaging and resting-state connectivity measures. Finally, we used these measures to predict behavioral performance with an XGBoost machine learning regression algorithm. Relative to controls, FLE (but not PCE) patients exhibited selective action-concept deficits together with structural and functional abnormalities along motor networks. The XGBoost model reached a significantly large effect size only for action-concept outcomes in FLE, mainly predicted by structural (cortico-spinal tract, anterior thalamic radiation, uncinate fasciculus) and functional (M1-parietal/supramarginal connectivity) motor networks. These results extend the DMGH, suggesting that action-concept deficits are dissociable markers of frontal/motor (relative to posterior) disruptions, directly related to the structural and functional integrity of motor networks, and traceable beyond canonical movement disorders.

Gadolinium-free assessment of synovitis using diffusion tensor imaging.

The dynamic contrast-enhanced (DCE)-MRI parameter Ktrans can quantify the intensity of synovial inflammation (synovitis) in knees with osteoarthritis (OA), but requires the use of gadolinium-based contrast agent (GBCA). Diffusion tensor imaging (DTI) measures the diffusion of water molecules with parameters mean diffusivity (MD) and fractional anisotropy (FA), and has been proposed as a method to detect synovial inflammation without the use of GBCA. The purpose of this study is to (1) determine the ability of DTI to quantify the intensity of synovitis in OA by comparing MD and FA with our imaging gold standard Ktrans within the synovium and (2) compare DTI and DCE-MRI measures with the semi-quantitative grading of OA severity with the Kellgren-Lawrence (KL) and MRI Osteoarthritis Knee Score (MOAKS) systems, in order to assess the relationship between synovitis intensity and OA severity. Within the synovium, MD showed a significant positive correlation with Ktrans (r= 0.79, p< 0.001), while FA showed a significant negative correlation with Ktrans (r= -0.72, p= 0.0026). These results show that DTI is able to quantify the intensity of synovitis within the whole synovium without the use of exogenous contrast agent. Additionally, MD, FA, and Ktrans values did not vary significantly when knees were separated by KL grade (p= 0.15, p= 0.32, p= 0.41, respectively), while MD (r= 0.60, p= 0.018) and Ktrans (r= 0.62, p= 0.013) had a significant positive correlation and FA (r= -0.53, p= 0.043) had a negative correlation with MOAKS. These comparisons indicate that quantitative measures of the intensity of synovitis may provide information in addition to morphological assessment to evaluate OA severity. Using DTI to quantify the intensity of synovitis without GBCA may be helpful to facilitate a broader clinical assessment of the severity of OA.