Cholinergic Nucleus 4 Degeneration and Cognitive Impairment in Isolated Rapid Eye Movement Sleep Behavior Disorder.

BACKGROUND: Cholinergic nucleus 4 (Ch4) degeneration is associated with cognitive impairment in Parkinson's disease and dementia with Lewy bodies, but it is unknown if Ch4 degeneration is also present in isolated rapid eye movement sleep behavior disorder (iRBD). OBJECTIVE: The aim was to determine if there is evidence of Ch4 degeneration in patients with iRBD and if it is associated with cognitive impairment. METHODS: We analyzed the clinical and neuropsychological data of 35 iRBD patients and 35 age- and sex-matched healthy controls. Regional gray matter density (GMD) was calculated for Ch4 using probabilistic maps applied to brain magnetic resonance imaging (MRI). RESULTS: Ch4 GMD was significantly lower in the iRBD group compared to controls (0.417 vs. 0.441, P = 0.02). Ch4 GMD was also found to be a significant predictor of letter number sequencing (ß-coefficient = 58.31, P = 0.026, 95% confidence interval [7.47, 109.15]), a measure of working memory. CONCLUSIONS: iRBD is associated with Ch4 degeneration, and Ch4 degeneration in iRBD is associated with impairment in working memory. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

MRI and CT Identify Isocitrate Dehydrogenase (IDH)-Mutant Lower-Grade Gliomas Misclassified to 1p/19q Codeletion Status with Fluorescence in Situ Hybridization.

Background Fluorescence in situ hybridization (FISH) is a standard method for 1p/19q codeletion testing in diffuse gliomas but occasionally renders erroneous results. Purpose To determine whether MRI/CT analysis identifies isocitrate dehydrogenase (IDH)-mutant gliomas misassigned to 1p/19q codeletion status with FISH. Materials and Methods Data in patients with IDH-mutant lower-grade gliomas (World Health Organization grade II/III) and 1p/19q codeletion status determined with FISH that were accrued from January 1, 2010 to October 1, 2017, were included in this retrospective study. Two neuroradiologist readers analyzed the pre-resection MRI findings (and CT findings, when available) to predict 1p/19q status (codeleted or noncodeleted) and provided a prediction confidence score (1 = low, 2 = moderate, 3 = high). Percentage concordance between the consensus neuroradiologist 1p/19q prediction and the FISH result was calculated. For gliomas where (a) consensus neuroradiologist 1p/19q prediction differed from the FISH result and (b) consensus neuroradiologist confidence score was 2 or greater, further 1p/19q testing was performed with chromosomal microarray analysis (CMA). Nine control specimens were randomly chosen from the remaining study sample for CMA. Percentage concordance between FISH and CMA among the CMA-tested cases was calculated. Results A total of 112 patients (median age, 38 years [interquartile range, 31-51 years]; 57 men) were evaluated (112 gliomas). Percentage concordance between the consensus neuroradiologist 1p/19q prediction and the FISH result was 84.8% (95 of 112; 95% confidence interval: 76.8%, 90.9%). Among the 17 neuroradiologist-FISH discordances, there were nine gliomas associated with a consensus neuroradiologist confidence score of 2 or greater. In six (66.7%) of these nine gliomas, the 1p/19q codeletion status as determined with CMA disagreed with the FISH result and agreed with the consensus neuroradiologist prediction. For the nine control specimens, there was 100% agreement between CMA and FISH for 1p/19q determination. Conclusion MRI and CT analysis can identify diffuse gliomas misassigned to 1p/19q codeletion status with fluorescence in situ hybridization (FISH). Further molecular testing should be considered for gliomas with discordant neuroimaging and FISH results. © RSNA, 2019 Online supplemental material is available for this article.

Test-retest reliability and long-term stability of three-tissue constrained spherical deconvolution methods for analyzing diffusion MRI data.

PURPOSE: Several recent studies have used a three-tissue constrained spherical deconvolution pipeline to obtain quantitative metrics of brain tissue microstructure from diffusion-weighted MRI data. The three tissue compartments, consisting of white matter, gray matter, and CSF-like (free water) signals, are potentially useful in the evaluation of brain microstructure in a range of pathologies. However, the reliability and long-term stability of these metrics have not yet been evaluated. METHODS: This study examined estimates of whole-brain microstructure for the three tissue compartments, in three separate test-retest cohorts. Each cohort had different lengths of time between baseline and retest, ranging from within the same scanning session in the shortest interval to 3 months in the longest interval. Each cohort was also collected with different acquisition parameters. RESULTS: The CSF-like compartment displayed the greatest reliability across all cohorts, with intraclass correlation coefficient (ICC) values being above 0.95 in each cohort. White matter-like and gray matter-like compartments both demonstrated very high reliability in the immediate cohort (both ICC > 0.90); however, this declined in the 3-month interval cohort to both compartments having ICC > 0.80. Regional CSF-like signal fraction was examined in bilateral hippocampus and had an ICC > 0.80 in each cohort. CONCLUSION: The three-tissue constrained spherical deconvolution techniques provide reliable and stable estimates of tissue-microstructure composition, up to 3 months longitudinally in a control population. This forms an important basis for further investigations using three-tissue constrained spherical deconvolution techniques to track changes in microstructure across a variety of brain pathologies.

Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign.

PURPOSE: The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. METHODS: Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10-3mm2/s (VADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. RESULTS: VADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with VADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited VADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. CONCLUSION: Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with VADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. VADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.

Lower volume, more impairment: reduced cholinergic basal forebrain grey matter density is associated with impaired cognition in Parkinson disease.

OBJECTIVE: A major contributor to dementia in Parkinson disease (PD) is degeneration of the cholinergic basal forebrain. This study determined whether cholinergic nucleus 4 (Ch4) density is associated with cognition in early and more advanced PD. METHODS: We analysed brain MRIs and neuropsychological test scores for 228 newly diagnosed PD participants from the Parkinson's Progression Markers Initiative (PPMI), 101 healthy controls from the PPMI and 125 more advanced PD patients from a local retrospective cohort. Cholinergic basal forebrain nuclei densities were determined by applying probabilistic maps to MPRAGE T1 sequences processed using voxel-based morphometry methods. Relationships between grey matter densities and cognitive scores were analysed using correlations and linear regression models. RESULTS: In more advanced PD, greater Ch4 density was associated with Montreal Cognitive Assessment (MoCA) score (ß=14.2; 95% CI=1.5 to 27.0; p=0.03), attention domain z-score (ß=3.2; 95% CI=0.8 to 5.5; p=0.008) and visuospatial domain z-score (ß=7.9; 95% CI=2.0 to 13.8; p=0.009). In the PPMI PD cohort, higher Ch4 was associated with higher scores on MoCA (ß=9.2; 95% CI=1.9 to 16.5; p=0.01), Judgement of Line Orientation (ß=20.4; 95% CI=13.8 to 27.0; p<0.001), Letter Number Sequencing (ß=16.5; 95% CI=9.5 to 23.4; p<0.001) and Symbol Digit Modalities Test (ß=41.8; 95% CI=18.7 to 65.0; p<0.001). These same relationships were observed in 97 PPMI PD participants at 4 years. There were no significant associations between Ch4 density and cognitive outcomes in healthy controls. CONCLUSION: In de novo and more advanced PD, lower Ch4 density is associated with impaired global cognition, attention and visuospatial function.

Thalamic connectivity in patients with essential tremor treated with MR imaging-guided focused ultrasound: in vivo fiber tracking by using diffusion-tensor MR imaging.

PURPOSE: To use diffusion-tensor (DT) magnetic resonance (MR) imaging in patients with essential tremor who were treated with transcranial MR imaging-guided focused ultrasound lesion inducement to identify the structural connectivity of the ventralis intermedius nucleus of the thalamus and determine how DT imaging changes correlated with tremor changes after lesion inducement. MATERIALS AND METHODS: With institutional review board approval, and with prospective informed consent, 15 patients with medication-refractory essential tremor were enrolled in a HIPAA-compliant pilot study and were treated with transcranial MR imaging-guided focused ultrasound surgery targeting the ventralis intermedius nucleus of the thalamus contralateral to their dominant hand. Fourteen patients were ultimately included. DT MR imaging studies at 3.0 T were performed preoperatively and 24 hours, 1 week, 1 month, and 3 months after the procedure. Fractional anisotropy (FA) maps were calculated from the DT imaging data sets for all time points in all patients. Voxels where FA consistently decreased over time were identified, and FA change in these voxels was correlated with clinical changes in tremor over the same period by using Pearson correlation. RESULTS: Ipsilateral brain structures that showed prespecified negative correlation values of FA over time of -0.5 or less included the pre- and postcentral subcortical white matter in the hand knob area; the region of the corticospinal tract in the centrum semiovale, in the posterior limb of the internal capsule, and in the cerebral peduncle; the thalamus; the region of the red nucleus; the location of the central tegmental tract; and the region of the inferior olive. The contralateral middle cerebellar peduncle and bilateral portions of the superior vermis also showed persistent decrease in FA over time. There was strong correlation between decrease in FA and clinical improvement in hand tremor 3 months after lesion inducement (P < .001). CONCLUSION: DT MR imaging after MR imaging-guided focused ultrasound thalamotomy depicts changes in specific brain structures. The magnitude of the DT imaging changes after thalamic lesion inducement correlates with the degree of clinical improvement in essential tremor.

Conduction Velocity, G-ratio, and Extracellular Water as Microstructural Characteristics of Autism Spectrum Disorder.

The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a novel metric termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel neuroimaging metric, aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.

Conduction velocity, G-ratio, and extracellular water as microstructural characteristics of autism spectrum disorder.

The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a new approach to calculating axonal conduction velocity termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel formulation for calculating aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.

An intracellular isotropic diffusion signal is positively associated with pubertal development in white matter.

Puberty is a key event in adolescent development that involves significant, hormone-driven changes to many aspects of physiology including the brain. Understanding how the brain responds during this time period is important for evaluating neuronal developments that affect mental health throughout adolescence and the adult lifespan. This study examines diffusion MRI scans from the cross-sectional ABCD Study baseline cohort, a large multi-site study containing thousands of participants, to describe the relationship between pubertal development and brain microstructure. Using advanced, 3-tissue constrained spherical deconvolution methods, this study is able to describe multiple tissue compartments beyond only white matter (WM) axonal qualities. After controlling for age, sex, brain volume, subject handedness, scanning site, and sibling relationships, we observe a positive relationship between an isotropic, intracellular diffusion signal fraction and pubertal development across a majority of regions of interest (ROIs) in the WM skeleton. We also observe regional effects from an intracellular anisotropic signal fraction compartment and extracellular isotropic free water-like compartment in several ROIs. This cross-sectional work suggests that changes in pubertal status are associated with a complex response from brain tissue that cannot be completely described by traditional methods focusing only on WM axonal properties.

Investigating the Effect of Brain Size on Deformation Magnitude Using Subject-Specific Finite Element Models.

Abstract In the last decade, computational models of the brain have become the gold standard tool for investigating traumatic brain injury (TBI) mechanisms and developing novel protective equipment and other safety countermeasures. However, most studies utilizing finite element (FE) models of the brain have been conducted using models developed to represent the average neuroanatomy of a target demographic, such as the 50th percentile male. Although this is an efficient strategy, it neglects normal anatomical variations present within the population and their contributions on the brain's deformation response. As a result, the contributions of structural characteristics of the brain, such as brain volume, on brain deformation are not well understood. The objective of this study was to develop a set of statistical regression models relating measures of the size and shape of the brain to the resulting brain deformation. This was performed using a database of 125 subject-specific models, simulated under six independent head kinematic boundary conditions, spanning a range of impact modes (frontal, oblique, side), severity (non-injurious and injurious), and environments (volunteer, automotive, and American football). Two statistical regression techniques were utilized. First, simple linear regression (SLR) models were trained to relate intracranial volume (ICV) and the 95th percentile of maximum principal strain (MPS-95) for each of the impact cases. Second, a partial least squares regression model was constructed to predict MPS-95 based on the affine transformation parameters from each subject, representing the size and shape of their brain, considering the six impact conditions collectively. Both techniques indicated a strong linear relationship between ICV and MPS-95, with MPS-95 varying by approximately 5% between the smallest and largest brains. This difference represented up to 40% of the mean strain across all subjects. This study represents a comprehensive assessment of the relationships between brain anatomy and deformation, which is crucial for the development of personalized protective equipment, identifying individuals at higher risk of injury, and using computational models to aid clinical diagnostics of TBI.

Concussion: Beyond the Cascade.

Sport concussion affects millions of athletes each year at all levels of sport. Increasing evidence demonstrates clinical and physiological recovery are becoming more divergent definitions, as evidenced by several studies examining blood-based biomarkers of inflammation and imaging studies of the central nervous system (CNS). Recent studies have shown elevated microglial activation in the CNS in active and retired American football players, as well as in active collegiate athletes who were diagnosed with a concussion and returned to sport. These data are supportive of discordance in clinical symptomology and the inflammatory response in the CNS upon symptom resolution. In this review, we will summarize recent advances in the understanding of the inflammatory response associated with sport concussion and broader mild traumatic brain injury, as well as provide an outlook for important research questions to better align clinical and physiological recovery.

An epigenetic mechanism for differential maturation of amygdala-prefrontal connectivity in childhood socio-emotional development.

Functional connectivity between the amygdala and the medial prefrontal cortex (mPFC) has been identified as a neural substrate of emotion regulation that undergoes changes throughout development, with a mature profile typically emerging at 10 years of age. Maternal bonding in childhood has been shown to buffer amygdala reactivity and to influence the trajectory of amygdala-mPFC coupling. The oxytocinergic system is critical in the development of social behavior and maternal bonding. Early-life parental care influences the methylation status of the oxytocin receptor (OXTRm) in animal models and humans, and higher OXTRm is associated with lower amygdala-PFC functional connectivity in adults. Using a neuroimaging-epigenetic approach, we investigated saliva-derived OXTRm as a biological marker of structural and functional connectivity maturation in 57 typically developing children (P < 0.05). We utilized seed-based connectivity analysis during a novel abstract movie paradigm and find that higher levels of OXTRm are associated with a more adult-like functional connectivity profile. Concurrently, more adult-like functional connectivity was associated with higher reported self-control and more diffusion streamlines between the amygdala and mPFC. OXTRm mediates the association between structural and functional connectivity with higher levels of OXTRm being associated with more streamlines. Lastly, we also find that lower OXTRm blunts the association between amygdala-mPFC connectivity and future internalizing behaviors in early adolescence. These findings implicate OXTRm as a biological marker at the interface of the social environment and amygdala-mPFC connectivity in emotional and behavioral regulation. Ultimately, identification of neurobiological markers may lead to earlier detection of children at risk for socio-emotional dysfunction.

Functional connectivity magnetic resonance imaging classification of autism.

Group differences in resting state functional magnetic resonance imaging connectivity between individuals with autism and typically developing controls have been widely replicated for a small number of discrete brain regions, yet the whole-brain distribution of connectivity abnormalities in autism is not well characterized. It is also unclear whether functional connectivity is sufficiently robust to be used as a diagnostic or prognostic metric in individual patients with autism. We obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the entire grey matter (26.4 million connections) in a well-characterized set of 40 male adolescents and young adults with autism and 40 age-, sex- and IQ-matched typically developing subjects. A single resting state blood oxygen level-dependent scan of 8 min was used for the classification in each subject. A leave-one-out classifier successfully distinguished autism from control subjects with 83% sensitivity and 75% specificity for a total accuracy of 79% (P = 1.1 × 10(-7)). In subjects <20 years of age, the classifier performed at 89% accuracy (P = 5.4 × 10(-7)). In a replication dataset consisting of 21 individuals from six families with both affected and unaffected siblings, the classifier performed at 71% accuracy (91% accuracy for subjects <20 years of age). Classification scores in subjects with autism were significantly correlated with the Social Responsiveness Scale (P = 0.05), verbal IQ (P = 0.02) and the Autism Diagnostic Observation Schedule-Generic's combined social and communication subscores (P = 0.05). An analysis of informative connections demonstrated that region of interest pairs with strongest correlation values were most abnormal in autism. Negatively correlated region of interest pairs showed higher correlation in autism (less anticorrelation), possibly representing weaker inhibitory connections, particularly for long connections (Euclidean distance >10 cm). Brain regions showing greatest differences included regions of the default mode network, superior parietal lobule, fusiform gyrus and anterior insula. Overall, classification accuracy was better for younger subjects, with differences between autism and control subjects diminishing after 19 years of age. Classification scores of unaffected siblings of individuals with autism were more similar to those of the control subjects than to those of the subjects with autism. These findings indicate feasibility of a functional connectivity magnetic resonance imaging diagnostic assay for autism.

Decreased interhemispheric functional connectivity in autism.

The cortical underconnectivity theory asserts that reduced long-range functional connectivity might contribute to a neural mechanism for autism. We examined resting-state blood oxygen level-dependent interhemispheric correlation in 53 males with high-functioning autism and 39 typically developing males from late childhood through early adulthood. By constructing spatial maps of correlation between homologous voxels in each hemisphere, we found significantly reduced interhemispheric correlation specific to regions with functional relevance to autism: sensorimotor cortex, anterior insula, fusiform gyrus, superior temporal gyrus, and superior parietal lobule. Observed interhemispheric connectivity differences were better explained by diagnosis of autism than by potentially confounding neuropsychological metrics of language, IQ, or handedness. Although both corpus callosal volume and gray matter interhemispheric connectivity were significantly reduced in autism, no direct relationship was observed between them, suggesting that structural and functional metrics measure different aspects of interhemispheric connectivity. In the control but not the autism sample, there was decreasing interhemispheric correlation with subject age. Greater differences in interhemispheric correlation were seen for more lateral regions in the brain. These findings suggest that long-range connectivity abnormalities in autism are spatially heterogeneous and that transcallosal connectivity is decreased most in regions with functions associated with behavioral abnormalities in autism. Autism subjects continue to show developmental differences in interhemispheric connectivity into early adulthood.

Network anticorrelations, global regression, and phase-shifted soft tissue correction.

Synchronized low-frequency BOLD fluctuations are observed in dissociable large-scale, distributed networks with functional specialization. Two such networks, referred to as the task-positive network (TPN) and the task-negative network (TNN) because they tend to be active or inactive during cognitively demanding tasks, show reproducible anticorrelation of resting BOLD fluctuations after removal of the global brain signal. Because global signal regression mandates that anticorrelated regions to a given seed region must exist, it is unclear whether such anticorrelations are an artifact of global regression or an intrinsic property of neural activity. In this study, we demonstrate from simulated data that spurious anticorrelations are introduced during global regression for any two networks as a linear function of their size. Using actual resting state data, we also show that both the TPN and TNN become anticorrelated with the orbits when soft tissues are included in the global regression algorithm. Finally, we propose a technique using phase-shifted soft tissue regression (PSTCor) that allows improved correction of global physiological artifacts without global regression that shows improved anatomic specificity to global regression but does not show significant network anticorrelations. These results imply that observed anticorrelations between TNN and TPN may be largely or entirely artifactual in the resting state. These results also imply that differences in network anticorrelations attributed to pathophysiological or behavioral states may be due to differences in network size or recruitment rather than actual anticorrelations.

Cholinergic nucleus 4 atrophy and gait impairment in Parkinson's disease.

BACKGROUND: There is evidence that cortical cholinergic denervation contributes to gait and balance impairment in Parkinson's Disease (PD), especially reduced gait speed. OBJECTIVES: The objective of this study was to determine the relationship between cholinergic basal forebrain gray matter density (GMD) and gait in PD patients. METHODS: We investigated 66 PD patients who underwent a pre-surgical evaluation for a neurosurgical procedure to treat motor symptoms of PD. As part of this evaluation patients had a brain MRI and formal gait assessments. By applying probabilistic maps of the cholinergic basal forebrain to voxel-based morphometry of brain MRI, we calculated gray matter density (GMD) for cholinergic nucleus 4 (Ch4), cholinergic nucleus 1, 2, and 3 (Ch123), and the entire cortex. RESULTS: Reduced Ch4 GMD was associated with reduced Fast Walking Speed in the "on" medication state (FWSON, p = 0.004). Bilateral cortical GMD was also associated with FWSON (p = 0.009), but Ch123 GMD was not (p = 0.1). Bilateral cortical GMD was not associated with FWSON after adjusting for Ch4 GMD (p = 0.44). While Ch4 GMD was not associated with improvement in Timed Up and Go (TUG) or Cognitive TUG in the "on" medication state, reduced Ch4 GMD was associated with greater percent worsening based on dual tasks (p = 0.021). CONCLUSIONS: Reduced Ch4 GMD is associated with slower gait speed in PD and greater percent worsening in TUG during dual tasks in patients with PD. These findings have implications for planning of future clinical trials investigating cholinergic therapies to improve gait impairment in PD.

Olfaction, cholinergic basal forebrain degeneration, and cognition in early Parkinson disease.

INTRODUCTION: Impaired olfaction and reduced cholinergic nucleus 4 (Ch4) volume both predict greater cognitive decline in Parkinson's disease (PD). We examined the relationship between olfaction, longitudinal change in cholinergic basal forebrain nuclei and their target regions, and cognition in early PD. METHODS: We analyzed a cohort of 97 PD participants from the Parkinson's Progression Markers Initiative with brain MRIs at baseline, 1 year, 2 years, and 4 years. Using probabilistic maps, regional grey matter density (GMD) was calculated for Ch4, cholinergic nuclei 1, 2, and 3 (Ch123), and their target regions. RESULTS: Baseline University of Pennsylvania Smell Identification Test score correlated with change in GMD of all regions of interest (all p < 0.05). Rate of change of Ch4 GMD was correlated with rate of change of Ch123 (p = 0.034), cortex (p = 0.001), and amygdala GMD (p < 0.001), but not hippocampus GMD (p = 0.38). Rate of change of Ch123 GMD was correlated with rate of change of cortex (p = 0.001) and hippocampus (p < 0.001), but not amygdala GMD (p = 0.133). In a linear regression model including change in GMD of all regions of interest and age as predictors, change in cortex GMD (߈slope= 38.2; 95 % CI: [0.47, 75.9]) and change in hippocampus GMD (߈slope= 24.8; 95 % CI: [0.80, 48.8]) were significant predictors of Montreal Cognitive Assessment score change over time. CONCLUSION: Impaired olfaction is associated with degeneration of the cholinergic basal forebrain and bilateral cortex, amygdala, and hippocampus in PD. The relationship between impaired olfaction and cognitive decline may be mediated by greater atrophy of the cortex and hippocampus.

Cytoarchitectonic Mapping of MRI Detects Rapid Changes in Alzheimer's Disease.

The clinical and pathological progression of Alzheimer's disease often proceeds rapidly, but little is understood about its structural characteristics over short intervals. This study evaluated the short temporal characteristics of the brain structure in Alzheimer's disease through the application of cytoarchitectonic probabilistic brain mapping to measurements of gray matter density, a technique which may provide advantages over standard volumetric MRI techniques. Gray matter density was calculated using voxel-based morphometry of T1-weighted MRI obtained from Alzheimer's disease patients and healthy controls evaluated at intervals of 0.5, 1.5, 3.5, 6.5, 9.5, 12, 18, and 24 months by the MIRIAD study. The Alzheimer's disease patients had 19.1% less gray matter at 1st MRI, and this declined 81.6% faster than in healthy controls. Atrophy in the hippocampus, amygdala, and basal forebrain distinguished the Alzheimer's disease patients. Notably, the CA2 of the hippocampus was found to have atrophied significantly within 1 month. Gray matter density measurements were reliable, with intraclass correlation coefficients exceeding 0.8. Comparative atrophy in the Alzheimer's disease group agreed with manual tracing MRI studies of Alzheimer's disease while identifying atrophy on a shorter time scale than has previously been reported. Cytoarchitectonic mapping of gray matter density is reliable and sensitive to small-scale neurodegeneration, indicating its use in the future study of Alzheimer's disease.

An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models.

Finite element (FE) models of the brain are crucial for investigating the mechanisms of traumatic brain injury (TBI). However, FE brain models are often limited to a single neuroanatomy because the manual development of subject-specific models is time consuming. The objective of this study was to develop a pipeline to automatically generate subject-specific FE brain models using previously developed nonlinear image registration techniques, preserving both external and internal neuroanatomical characteristics. To verify the morphing-induced mesh distortions did not influence the brain deformation response, strain distributions predicted using the morphed model were compared to those from manually created voxel models of the same subject. Morphed and voxel models were generated for 44 subjects ranging in age, and simulated using head kinematics from a football concussion case. For each subject, brain strain distributions predicted by each model type were consistent, and differences in strain prediction was less than 4% between model type. This automated technique, taking approximately 2 h to generate a subject-specific model, will facilitate interdisciplinary research between the biomechanics and neuroimaging fields and could enable future use of biomechanical models in the clinical setting as a tool for improving diagnosis.

Brain MRI Reveals Ascending Atrophy in Parkinson's Disease Across Severity.

Models which assess the progression of Lewy pathology in Parkinson's disease have proposed ascending spread in a caudal-rostral pattern. In-vivo human evidence for this theory is limited, in part because there are no biomarkers that allow for direct assessment of Lewy pathology. Here, we measured neurodegeneration via MRI, an outcome which may serve as a proxy for a more direct assessment of ascending models using a combination of (1) MRI-based measures of gray matter density and (2) regions of interest (ROIs) corresponding to cortical and subcortical loci implicated in past MRI and stereological studies of Parkinson's disease. Gray matter density was measured using brain MRI voxel-based morphometry from three cohorts: (1) early Parkinson's disease, (2) more advanced Parkinson's disease and (3) healthy controls. Early Parkinson's disease patients (N = 228, mean age = 61.9 years, mean disease duration = 0.6 years) were newly diagnosed by the Parkinson's Progression Markers Initiative (PPMI). Advanced Parkinson's disease patients (N = 136, mean age = 63.5 years, mean disease duration = 8.0 years) were collected retrospectively from a local cohort undergoing evaluation for functional neurosurgery. Control subjects (N = 103, mean age = 60.2 years) were from PPMI. Comparative analyses focused on gray matter regions ranging from deep gray subcortical structures to the neocortex. ROIs were defined with existing probabilistic cytoarchitectonic brain maps. For subcortical regions of the basal forebrain, amygdala, and entorhinal cortex, advanced Parkinson's disease patients had significantly lower gray matter density when compared to both early Parkinson's disease and healthy controls. No differences were seen in neocortical regions that are "higher" in any proposed ascending pattern. Across early and advanced Parkinson's disease, gray matter density from nearly all subcortical regions significantly decreased with disease duration; no neocortical regions showed this effect. These results demonstrate that atrophy in advanced Parkinson's patients compared to early patients and healthy controls is largely confined to subcortical gray matter structures. The degree of atrophy in subcortical brain regions was linked to overall disease duration, suggesting an organized pattern of atrophy across severity.