A practical model-based segmentation approach for improved activation detection in single-subject functional magnetic resonance imaging studies.

Functional magnetic resonance imaging (fMRI) maps cerebral activation in response to stimuli but this activation is often difficult to detect, especially in low-signal contexts and single-subject studies. Accurate activation detection can be guided by the fact that very few voxels are, in reality, truly activated and that these voxels are spatially localized, but it is challenging to incorporate both these facts. We address these twin challenges to single-subject and low-signal fMRI by developing a computationally feasible and methodologically sound model-based approach, implemented in the R package MixfMRI, that bounds the a priori expected proportion of activated voxels while also incorporating spatial context. An added benefit of our methodology is the ability to distinguish voxels and regions having different intensities of activation. Our suggested approach is evaluated in realistic two- and three-dimensional simulation experiments as well as on multiple real-world datasets. Finally, the value of our suggested approach in low-signal and single-subject fMRI studies is illustrated on a sports imagination experiment that is often used to detect awareness and improve treatment in patients in persistent vegetative state (PVS). Our ability to reliably distinguish activation in this experiment potentially opens the door to the adoption of fMRI as a clinical tool for the improved treatment and therapy of PVS survivors and other patients.

Increased Cortical Activity in Novices Compared to Experts During Table Tennis: A Whole-Brain fNIRS Study Using Threshold-Free Cluster Enhancement Analysis.

There is a growing interest to understand the neural underpinnings of high-level sports performance including expertise-related differences in sport-specific skills. Here, we aimed to investigate whether expertise level and task complexity modulate the cortical hemodynamics of table tennis players. 35 right-handed table tennis players (17 experts/18 novices) were recruited and performed two table tennis strokes (forehand and backhand) and a randomized combination of them. Cortical hemodynamics, as a proxy for cortical activity, were recorded using functional near-infrared spectroscopy, and the behavioral performance (i.e., target accuracy) was assessed via video recordings. Expertise- and task-related differences in cortical hemodynamics were analyzed using nonparametric threshold-free cluster enhancement. In all conditions, table tennis experts showed a higher target accuracy than novices. Furthermore, we observed expertise-related differences in widespread clusters compromising brain areas being associated with sensorimotor and multisensory integration. Novices exhibited, in general, higher activation in those areas as compared to experts. We also identified task-related differences in cortical activity including frontal, sensorimotor, and multisensory brain areas. The present findings provide empirical support for the neural efficiency hypothesis since table tennis experts as compared to novices utilized a lower amount of cortical resources to achieve superior behavioral performance. Furthermore, our findings suggest that the task complexity of different table tennis strokes is mirrored in distinct cortical activation patterns. Whether the latter findings can be useful to monitor or tailor sport-specific training interventions necessitates further investigations.

Evaluation of thresholding methods for activation likelihood estimation meta-analysis via large-scale simulations.

In recent neuroimaging studies, threshold-free cluster enhancement (TFCE) gained popularity as a sophisticated thresholding method for statistical inference. It was shown to feature higher sensitivity than the frequently used approach of controlling the cluster-level family-wise error (cFWE) and it does not require setting a cluster-forming threshold at voxel level. Here, we examined the applicability of TFCE to a widely used method for coordinate-based neuroimaging meta-analysis, Activation Likelihood Estimation (ALE), by means of large-scale simulations. We created over 200,000 artificial meta-analysis datasets by independently varying the total number of experiments included and the amount of spatial convergence across experiments. Next, we applied ALE to all datasets and compared the performance of TFCE to both voxel-level and cluster-level FWE correction approaches. All three multiple-comparison correction methods yielded valid results, with only about 5% of the significant clusters being based on spurious convergence, which corresponds to the nominal level the methods were controlling for. On average, TFCE's sensitivity was comparable to that of cFWE correction, but it was slightly worse for a subset of parameter combinations, even after TFCE parameter optimization. cFWE yielded the largest significant clusters, closely followed by TFCE, while voxel-level FWE correction yielded substantially smaller clusters, showcasing its high spatial specificity. Given that TFCE does not outperform the standard cFWE correction but is computationally much more expensive, we conclude that employing TFCE for ALE cannot be recommended to the general user.

Data-point-wise spatiotemporal mapping of human ventral visual areas: Use of spatial frequency/luminance-modulated chromatic faces.

Visual information involving facial identity and expression is crucial for social communication. Although the influence of facial features such as spatial frequency (SF) and luminance on face processing in visual areas has been studied extensively using grayscale stimuli, the combined effects of other features in this process have not been characterized. To determine the combined effects of different SFs and color, we created chromatic stimuli with low, high or no SF components, which bring distinct SF and color information into the ventral stream simultaneously. To obtain neural activity data with high spatiotemporal resolution we recorded face-selective responses (M170) using magnetoencephalography. We used a permutation test procedure with threshold-free cluster enhancement to assess statistical significance while resolving problems related to multiple comparisons and arbitrariness found in traditional statistical methods. We found that time windows with statistically significant threshold levels were distributed differently among the stimulus conditions. Face stimuli containing any SF components evoked M170 in the fusiform gyrus (FG), whereas a significant emotional effect on M170 was only observed with the original images. Low SF faces elicited larger activation of the FG and the inferior occipital gyrus than the original images, suggesting an interaction between low and high SF information processing. Interestingly, chromatic face stimuli without SF first activated color-selective regions and then the FG, indicating that facial color was processed according to a hierarchy in the ventral stream. These findings suggest complex effects of SFs in the presence of color information, reflected in M170, and unveil the detailed spatiotemporal dynamics of face processing in the human brain.

Probabilistic TFCE: A generalized combination of cluster size and voxel intensity to increase statistical power.

The threshold-free cluster enhancement (TFCE) approach integrates cluster information into voxel-wise statistical inference to enhance detectability of neuroimaging signal. Despite the significantly increased sensitivity, the application of TFCE is limited by several factors: (i) generalisation to data structures, like brain network connectivity data is not trivial, (ii) TFCE values are in an arbitrary unit, therefore, P-values can only be obtained by a computationally demanding permutation-test. Here, we introduce a probabilistic approach for TFCE (pTFCE), that gives a simple general framework for topology-based belief boosting. The core of pTFCE is a conditional probability, calculated based on Bayes' rule, from the probability of voxel intensity and the threshold-wise likelihood function of the measured cluster size. In this paper, we provide an estimation of these distributions based on Gaussian Random Field theory. The conditional probabilities are then aggregated across cluster-forming thresholds by a novel incremental aggregation method. pTFCE is validated on simulated and real fMRI data. The results suggest that pTFCE is more robust to various ground truth shapes and provides a stricter control over cluster "leaking" than TFCE and, in many realistic cases, further improves its sensitivity. Correction for multiple comparisons can be trivially performed on the enhanced P-values, without the need for permutation testing, thus pTFCE is well-suitable for the improvement of statistical inference in any neuroimaging workflow. Implementation of pTFCE is available at https://spisakt.github.io/pTFCE.

Statistical inference in brain graphs using threshold-free network-based statistics.

The description of brain networks as graphs where nodes represent different brain regions and edges represent a measure of connectivity between a pair of nodes is an increasingly used approach in neuroimaging research. The development of powerful methods for edge-wise group-level statistical inference in brain graphs while controlling for multiple-testing associated false-positive rates, however, remains a difficult task. In this study, we use simulated data to assess the properties of threshold-free network-based statistics (TFNBS). The TFNBS combines threshold-free cluster enhancement, a method commonly used in voxel-wise statistical inference, and network-based statistic (NBS), which is frequently used for statistical analysis of brain graphs. Unlike the NBS, TFNBS generates edge-wise significance values and does not require the a priori definition of a hard cluster-defining threshold. Other test parameters, nonetheless, need to be set. We show that it is possible to find parameters that make TFNBS sensitive to strong and topologically clustered effects, while appropriately controlling false-positive rates. Our results show that the TFNBS is an adequate technique for the statistical assessment of brain graphs.

Statistical Permutation-based Artery Mapping (SPAM): a novel approach to evaluate imaging signals in the vessel wall.

BACKGROUND: Cardiovascular diseases are the leading cause of death worldwide. A prominent cause of cardiovascular events is atherosclerosis, a chronic inflammation of the arterial wall that leads to the formation of so called atherosclerotic plaques. There is a strong clinical need to develop new, non-invasive vascular imaging techniques in order to identify high-risk plaques, which might escape detection using conventional methods based on the assessment of the luminal narrowing. In this context, molecular imaging strategies based on fluorescent tracers and fluorescence reflectance imaging (FRI) seem well suited to assess molecular and cellular activity. However, such an analysis demands a precise and standardized analysis method, which is orientated on reproducible anatomical landmarks, ensuring to compare equivalent regions across different subjects. METHODS: We propose a novel method, Statistical Permutation-based Artery Mapping (SPAM). Our approach is especially useful for the understanding of complex and heterogeneous regional processes during the course of atherosclerosis. Our method involves three steps, which are (I) standardisation with an additional intensity normalization, (II) permutation testing, and (III) cluster-enhancement. Although permutation testing and cluster enhancement are already well-established in functional magnetic resonance imaging, to the best of our knowledge these strategies have so far not been applied in cardiovascular molecular imaging. RESULTS: We tested our method using FRI images of murine aortic vessels in order to find recurring patterns in atherosclerotic plaques across multiple subjects. We demonstrate that our pixel-wise and cluster-enhanced testing approach is feasible and useful to analyse tracer distributions in FRI data sets of aortic vessels. CONCLUSIONS: We expect our method to be a useful tool within the field of molecular imaging of atherosclerotic plaques since cluster-enhanced permutation testing is a powerful approach for finding significant differences of tracer distributions in inflamed atherosclerotic vessels.

Validity of modulation and optimal settings for advanced voxel-based morphometry.

Voxel-based morphometry (VBM) is a widely-used structural neuroimaging technique for comparing meso- and macroscopic regional brain volumes between patients and controls in vivo, but some of its steps, particularly the modulation, lack an experimental validation. The aims of this study were two-fold: a) to assess the effects of modulation to detect mesoscopic (i.e. between microscopic and macroscopic) abnormalities on published, classic VBM; and b) to suggest a set of potentially optimal settings for detecting mesoscopic abnormalities with new, advanced, high-resolution diffeomorphic VBM normalization algorithms. Sensitivity and false positive rate after modulating or not in classic VBM using different software packages and spatial statistics, and after setting a range of different parameters in advanced VBM (ANTS-SyN), were calculated in 10 VBM comparisons of 32 altered vs. 32 unaltered gray matter images from different healthy controls. Simulated brain abnormalities comprised mesoscopic volume differences mainly due to cortical thinning. In classic VBM, modulation was associated with a substantial decrease of the sensitivity to detect mesoscopic abnormalities (p<0.001). Optimal settings for advanced VBM included the omission of modulation, the use of large smoothing kernels, and the application of voxel-based or threshold-free cluster enhancement (TFCE) spatial statistics. The modulation-related decrease in sensitivity was due to an increase in variance, and it was more severe in higher-resolution normalization algorithms. Findings from this study suggest the use of unmodulated VBM to detect mesoscopic abnormalities such as cortical thinning.

Resting-State Functional Connectivity Difference in Alzheimer's Disease and Mild Cognitive Impairment Using Threshold-Free Cluster Enhancement.

The disruption of functional connectivity is one of the early events that occurs in the brains of Alzheimer's disease (AD) patients. This paper reports a study on the clustering structure of functional connectivity in eight important brain networks in healthy, AD, and prodromal stage subjects. We used the threshold-free cluster enhancement (TFCE) method to explore the connectivity from resting-state functional MR images (rs-fMRIs). We conducted the study on a total of 32 AD, 32 HC, and 31 MCI subjects. We modeled the brain as a graph-based network to study these impairments, and pairwise Pearson's correlation-based functional connectivity was used to construct the brain network. The study found that connections in the sensory motor network (SMN), dorsal attention network (DAN), salience network (SAN), default mode network (DMN), and cerebral network were severely affected in AD and MCI. The disruption in these networks may serve as potential biomarkers for distinguishing AD and MCI from HC. The study suggests that alterations in functional connectivity in these networks may contribute to cognitive deficits observed in AD and MCI. Additionally, a negative correlation was observed between the global clinical dementia rating (CDR) score and the Z-score of functional connectivity within identified clusters in AD subjects. These findings provide compelling evidence suggesting that the neurodegenerative disruption of functional magnetic resonance imaging (fMRI) connectivity is extensively distributed across multiple networks in individuals diagnosed with AD.

Increased subcortical brain activity in anxious but not depressed individuals.

BACKGROUND: Anxiety and depressive symptoms usually co-occur. Neuroimaging abnormalities in patients with depression and anxiety disorders are therefore related to a combination of symptoms. Here, we used a large population study to select individuals with anxiety, depressive, or both anxiety and depressive symptoms to identify whether neuroimaging differences are unique or shared between anxiety and depressive symptoms. METHODS: We selected four groups of 200 individuals (anxiety, depression, anxiety and depression, controls) from the UK Biobank, matched for age, sex, intelligence, and educational attainment (total N = 800). We extracted the amplitude of low frequency fluctuations (ALFF) from resting-state functional magnetic resonance imaging data, which indexes spontaneous neuronal activity. Group differences were assessed using permutation testing to correct for multiple comparisons, with age, sex, IQ, and head motion as covariates. RESULTS: Compared to controls, anxious individuals had higher ALFF values in many subcortical brain regions including the striatum, thalamus, medial temporal lobe, midbrain, pons, as well as the cerebellum. Anxious individuals also showed higher ALFF in the hippocampus, parahippocampal gyrus, cerebellum, and pons compared to individuals with depressive symptoms. No significant differences were found for the depression and combined anxiety/depression groups. Post-hoc tests with largest possible samples showed comparable results in the anxiety group and in the combined group, but still no significant differences for the depression group. CONCLUSIONS: Anxiety but not depressive symptoms were associated with increased subcortical activity during rest. This suggest that anxiety symptoms may have the largest contribution to the neuroimaging differences in individuals with depression and anxiety disorders.

Amygdala size varies with stress perception.

Stress is inevitably linked to life. It has many and complex facets. Notably, perception of stressful stimuli is an important factor when mounting stress responses and measuring its impact. Indeed, moved by the increasing number of stress-triggered pathologies, several groups drew on advanced neuroimaging techniques to explore stress effects on the brain. From that, several regions and circuits have been linked to stress, and a comprehensive integration of the distinct findings applied to common individuals is being pursued, but with conflicting results. Herein, we performed a volumetric regression analysis using participants' perceived stress as a variable of interest. Data shows that increased levels of perceived stress positively associate with the right amygdala and anterior hippocampal volumes.

Computational Methods for Continuous Eye-Tracking Perimetry Based on Spatio-Temporal Integration and a Deep Recurrent Neural Network.

The measurement of retinal sensitivity at different visual field locations-perimetry-is a fundamental procedure in ophthalmology. The most common technique for this scope, the Standard Automated Perimetry, suffers from several issues that make it less suitable to test specific clinical populations: it can be tedious, it requires motor manual feedback, and requires from the patient high levels of compliance. Previous studies attempted to create user-friendlier alternatives to Standard Automated Perimetry by employing eye movements reaction times as a substitute for manual responses while keeping the fixed-grid stimuli presentation typical of Standard Automated Perimetry. This approach, however, does not take advantage of the high spatial and temporal resolution enabled by the use of eye-tracking. In this study, we introduce a novel eye-tracking method to perform high-resolution perimetry. This method is based on the continuous gaze-tracking of a stimulus moving along a pseudo-random walk interleaved with saccadic jumps. We then propose two computational methods to obtain visual field maps from the continuous gaze-tracking data: the first is based on the spatio-temporal integration of ocular positional deviations using the threshold free cluster enhancement (TFCE) algorithm; the second is based on using simulated visual field defects to train a deep recurrent neural network (RNN). These two methods have complementary qualities: the TFCE is neurophysiologically plausible and its output significantly correlates with Standard Automated Perimetry performed with the Humphrey Field Analyzer, while the RNN accuracy significantly outperformed the TFCE in reconstructing the simulated scotomas but did not translate as well to the clinical data from glaucoma patients. While both of these methods require further optimization, they show the potential for a more patient-friendly alternative to Standard Automated Perimetry.

Chronic oxytocin administration in older men modulates functional connectivity during animacy perception.

While aging is associated with social-cognitive change and oxytocin plays a crucial role in social cognition, oxytocin's effects on the social brain in older age remain understudied. To date, no study has examined the effects of chronic intranasal oxytocin administration on brain mechanisms underlying animacy perception in older adults. Using a placebo-controlled, randomized, double-blinded design in generally healthy older men (mean age (SD) = 69(6); n = 17 oxytocin; n = 14 placebo), this study determined the effects of a four-week intranasal oxytocin administration (24 international units/twice a day) on functional MRI (fMRI) during the Heider-Simmel task. This passive-viewing animacy perception paradigm contains video-clips of simple shapes suggesting social interactions (SOCIAL condition) or exhibiting random trajectories (RANDOM condition). While there were no oxytocin-specific effects on brain fMRI activation during the SOCIAL compared to the RANDOM condition, pre-to-post intervention change in the SOCIAL-RANDOM difference in functional connectivity (FC) was higher in the oxytocin compared to the placebo group in a network covering occipital, temporal, and parietal areas, and the superior temporal sulcus, a key structure in animacy perception. These findings suggest oxytocin modulation of circuits involved in action observation and social perception. Follow-up analyses on this network's connections suggested a pre-to-post intervention decrease in the SOCIAL-RANDOM difference in FC among the placebo group, possibly reflecting habituation to repeated exposure to social cues. Chronic oxytocin appeared to counter this process by decreasing FC during the RANDOM and increasing it during the SOCIAL condition. This study advances knowledge about oxytocin intervention mechanisms in the social brain of older adults.

Morphological changes after cranial fractionated photon radiotherapy: Localized loss of white matter and grey matter volume with increasing dose.

PURPOSE: Numerous brain MR imaging studies have been performed to understand radiation-induced cognitive decline. However, many of them focus on a single region of interest, e.g. cerebral cortex or hippocampus. In this study, we use deformation-based morphometry (DBM) and voxel-based morphometry (VBM) to measure the morphological changes in patients receiving fractionated photon RT, and relate these to the dose. Additionally, we study tissue specific volume changes in white matter (WM), grey matter (GM), cerebrospinal fluid and total intracranial volume (TIV). METHODS AND MATERIALS: From our database, we selected 28 patients with MRI of high quality available at baseline and 1 year after RT. Scans were rigidly registered to each other, and to the planning CT and dose file. We used DBM to study non-tissue-specific volumetric changes, and VBM to study volume loss in grey matter. Observed changes were then related to the applied radiation dose (in EQD2). Additionally, brain tissue was segmented into WM, GM and cerebrospinal fluid, and changes in these volumes and TIV were tested. RESULTS: Performing DBM resulted in clusters of dose-dependent volume loss 1 year after RT seen throughout the brain. Both WM and GM were affected; within the latter both cerebral cortex and subcortical nuclei show volume loss. Volume loss rates ranging from 5.3 to 15.3%/30 Gy were seen in the cerebral cortical regions in which more than 40% of voxels were affected. In VBM, similar loss rates were seen in the cortex and nuclei. The total volume of WM and GM significantly decreased with rates of 5.8% and 2.1%, while TIV remained unchanged as expected. CONCLUSIONS: Radiotherapy is associated with dose-dependent intracranial morphological changes throughout the entire brain. Therefore, we will consider to revise sparing of organs at risk based on future cognitive and neurofunctional data.

Action selection conflict and intentional binding: An ERP study.

The fluency with which we plan and execute actions has been demonstrated to increase our sense of agency (SoA). However, the exact mechanisms how fluency influences SoA are still poorly understood. It is an open question whether this effect is primarily driven by fluency of stimulus processing, response preparation or by processes following response execution. In the current study we aim at addressing this question by measuring event-related potentials reflecting pre- and post-response mechanisms and relate them to intentional binding, a measure of implicit SoA. To manipulate the fluency of action we asked participants to perform actions that were congruent or incongruent with a visual target (a finger movement). Participants' actions triggered an auditory outcome. To measure the intentional binding effect we asked participants to estimate the time between the executed actions and the ensuing auditory effects. We found that congruent actions generated a larger intentional binding effect (i.e., stronger time compression between actions and effects) and this positively correlated with a late P300 evoked during the processing of congruent stimuli. At the action selection level, we found a larger central pre-response positivity for incongruent condition as relates to interference effects. Finally, post response mechanisms elicited a larger central negativity for incongruent responses presumably related to uncertainty. We provide new evidence on the determinants of intentional binding driven by the fluency of action, by showing that both pre and post-response mechanisms are crucial in the generation of the feelings of agency. Importantly, stimulus processing and response preparation ERPs seem to be more selectively modulated by congruency-effects given specific brain-behavioral correlations.

Evaluating Alternative Correction Methods for Multiple Comparison in Functional Neuroimaging Research.

A significant challenge for fMRI research is statistically controlling for false positives without omitting true effects. Although a number of traditional methods for multiple comparison correction exist, several alternative tools have been developed that do not rely on strict parametric assumptions, but instead implement alternative methods to correct for multiple comparisons. In this study, we evaluated three of these methods, Statistical non-Parametric Mapping (SnPM), 3DClustSim, and Threshold Free Cluster Enhancement (TFCE), by examining which method produced the most consistent outcomes even when spatially-autocorrelated noise was added to the original images. We assessed the false alarm rate and hit rate of each method after noise was applied to the original images.

An Updated Survey on Statistical Thresholding and Sample Size of fMRI Studies.

Background: Since the early 2010s, the neuroimaging field has paid more attention to the issue of false positives. Several journals have issued guidelines regarding statistical thresholds. Three papers have reported the statistical analysis of the thresholds used in fMRI literature, but they were published at least 3 years ago and surveyed papers published during 2007-2012. This study revisited this topic to evaluate the changes in this field. Methods: The PubMed database was searched to identify the task-based (not resting-state) fMRI papers published in 2017 and record their sample sizes, inferential methods (e.g., voxelwise or clusterwise), theoretical methods (e.g., parametric or non-parametric), significance level, cluster-defining primary threshold (CDT), volume of analysis (whole brain or region of interest) and software used. Results: The majority (95.6%) of the 388 analyzed articles reported statistics corrected for multiple comparisons. A large proportion (69.6%) of the 388 articles reported main results by clusterwise inference. The analyzed articles mostly used software Statistical Parametric Mapping (SPM), Analysis of Functional NeuroImages (AFNI), or FMRIB Software Library (FSL) to conduct statistical analysis. There were 70.9%, 37.6%, and 23.1% of SPM, AFNI, and FSL studies, respectively, that used a CDT of p ≤ 0.001. The statistical sample size across the articles ranged between 7 and 1,299 with a median of 33. Sample size did not significantly correlate with the level of statistical threshold. Conclusion: There were still around 53% (142/270) studies using clusterwise inference that chose a more liberal CDT than p = 0.001 (n = 121) or did not report their CDT (n = 21), down from around 61% reported by Woo et al. (2014). For FSL studies, it seemed that the CDT practice had no improvement since the survey by Woo et al. (2014). A few studies chose unconventional CDT such as p = 0.0125 or 0.004. Such practice might create an impression that the threshold alterations were attempted to show "desired" clusters. The median sample size used in the analyzed articles was similar to those reported in previous surveys. In conclusion, there seemed to be no change in the statistical practice compared to the early 2010s.

Iron-related gene variants and brain iron in multiple sclerosis and healthy individuals.

Brain iron homeostasis is known to be disturbed in multiple sclerosis (MS), yet little is known about the association of common gene variants linked to iron regulation and pathological tissue changes in the brain. In this study, we investigated the association of genetic determinants linked to iron regulation with deep gray matter (GM) magnetic susceptibility in both healthy controls (HC) and MS patients. Four hundred (400) patients with MS and 150 age- and sex-matched HCs were enrolled and obtained 3 T MRI examination. Three (3) single nucleotide polymorphisms (SNPs) associated with iron regulation were genotyped: two SNPs in the human hereditary hemochromatosis protein gene HFE: rs1800562 (C282Y mutation) and rs1799945 (H63D mutation), as well as the rs1049296 SNP in the transferrin gene (C2 mutation). The effects of disease and genetic status were studied using quantitative susceptibility mapping (QSM) voxel-based analysis (VBA) and region-of-interest (ROI) analysis of the deep GM. The general linear model framework was used to compare groups. Analyses were corrected for age and sex, and adjusted for false discovery rate. We found moderate increases in susceptibility in the right putamen of participants with the C282Y (+ 6.1 ppb) and H63D (+ 6.9 ppb) gene variants vs. non-carriers, as well as a decrease in thalamic susceptibility of progressive MS patients with the C282Y mutation (left: - 5.3 ppb, right: - 6.7 ppb, p < 0.05). Female MS patients had lower susceptibility in the caudate (- 6.0 ppb) and putamen (left: - 3.9 ppb, right: - 4.6 ppb) than men, but only when they had a wild-type allele (p < 0.05). Iron-gene linked increases in putamen susceptibility (in HC and relapsing remitting MS) and decreases in thalamus susceptibility (in progressive MS), coupled with apparent sex interactions, indicate that brain iron in healthy and disease states may be influenced by genetic factors.

Connectivity derived thalamic segmentation in deep brain stimulation for tremor.

The ventral intermediate nucleus (VIM) of the thalamus is an established surgical target for stereotactic ablation and deep brain stimulation (DBS) in the treatment of tremor in Parkinson's disease (PD) and essential tremor (ET). It is centrally placed on a cerebello-thalamo-cortical network connecting the primary motor cortex, to the dentate nucleus of the contralateral cerebellum through the dentato-rubro-thalamic tract (DRT). The VIM is not readily visible on conventional MR imaging, so identifying the surgical target traditionally involved indirect targeting that relies on atlas-defined coordinates. Unfortunately, this approach does not fully account for individual variability and requires surgery to be performed with the patient awake to allow for intraoperative targeting confirmation. The aim of this study is to identify the VIM and the DRT using probabilistic tractography in patients that will undergo thalamic DBS for tremor. Four male patients with tremor dominant PD and five patients (three female) with ET underwent high angular resolution diffusion imaging (HARDI) (128 diffusion directions, 1.5 mm isotropic voxels and b value = 1500) preoperatively. Patients received VIM-DBS using an MR image guided and MR image verified approach with indirect targeting. Postoperatively, using parallel Graphical Processing Unit (GPU) processing, thalamic areas with the highest diffusion connectivity to the primary motor area (M1), supplementary motor area (SMA), primary sensory area (S1) and contralateral dentate nucleus were identified. Additionally, volume of tissue activation (VTA) corresponding to active DBS contacts were modelled. Response to treatment was defined as 40% reduction in the total Fahn-Tolosa-Martin Tremor Rating Score (FTMTRS) with DBS-ON, one year from surgery. Three out of nine patients had a suboptimal, long-term response to treatment. The segmented thalamic areas corresponded well to anatomically known counterparts in the ventrolateral (VL) and ventroposterior (VP) thalamus. The dentate-thalamic area, lay within the M1-thalamic area in a ventral and lateral location. Streamlines corresponding to the DRT connected M1 to the contralateral dentate nucleus via the dentate-thalamic area, clearly crossing the midline in the mesencephalon. Good response was seen when the active contact VTA was in the thalamic area with highest connectivity to the contralateral dentate nucleus. Non-responders had active contact VTAs outside the dentate-thalamic area. We conclude that probabilistic tractography techniques can be used to segment the VL and VP thalamus based on cortical and cerebellar connectivity. The thalamic area, best representing the VIM, is connected to the contralateral dentate cerebellar nucleus. Connectivity based segmentation of the VIM can be achieved in individual patients in a clinically feasible timescale, using HARDI and high performance computing with parallel GPU processing. This same technique can map out the DRT tract with clear mesencephalic crossing.

Loss of integrity and atrophy in cingulate structural covariance networks in Parkinson's disease.

BACKGROUND: In Parkinson's disease (PD), the relation between cortical brain atrophy on MRI and clinical progression is not straightforward. Determination of changes in structural covariance networks - patterns of covariance in grey matter density - has shown to be a valuable technique to detect subtle grey matter variations. We evaluated how structural network integrity in PD is related to clinical data. METHODS: 3 Tesla MRI was performed in 159 PD patients. We used nine standardized structural covariance networks identified in 370 healthy subjects as a template in the analysis of the PD data. Clinical assessment comprised motor features (Movement Disorder Society-Unified Parkinson's Disease Rating Scale; MDS-UPDRS motor scale) and predominantly non-dopaminergic features (SEverity of Non-dopaminergic Symptoms in Parkinson's Disease; SENS-PD scale: postural instability and gait difficulty, psychotic symptoms, excessive daytime sleepiness, autonomic dysfunction, cognitive impairment and depressive symptoms). Voxel-based analyses were performed within networks significantly associated with PD. RESULTS: The anterior and posterior cingulate network showed decreased integrity, associated with the SENS-PD score, p = 0.001 (ß = - 0.265, ηp2 = 0.070) and p = 0.001 (ß = - 0.264, ηp2 = 0.074), respectively. Of the components of the SENS-PD score, cognitive impairment and excessive daytime sleepiness were associated with atrophy within both networks. CONCLUSIONS: We identified loss of integrity and atrophy in the anterior and posterior cingulate networks in PD patients. Abnormalities of both networks were associated with predominantly non-dopaminergic features, specifically cognition and excessive daytime sleepiness. Our findings suggest that (components of) the cingulate networks display a specific vulnerability to the pathobiology of PD and may operate as interfaces between networks involved in cognition and alertness.