Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available.
multivariate analysis; multiple comparisons; multimodality imaging; diffusion tensor imaging; structural magnetic resonance imaging; perfusion weighted magnetic resonance imaging; Alzheimer's disease
Attention deficit hyperactive disorder (ADHD) and Autism spectrum disorders (ASD) are two of the most common and vexing neurodevelopmental disorders among children. Although the two disorders share many behavioral and neuropsychological characteristics, most MRI studies examine only one of the disorders at a time. Using graph theory combined with structural and functional connectivity, we examined the large-scale network organization among three groups of children: a group with ADHD (8-12 years, n = 20), a group with ASD (7-13 years, n = 16), and typically developing controls (TD) (8-12 years, n = 20). We apply the concept of the rich-club organization, whereby central, highly connected hub regions are also highly connected to themselves. We examine the brain into two different network domains: (1) inside a rich-club network phenomena, and (2) outside a rich-club network phenomena. ASD and ADHD populations had markedly different patterns of rich club and non rich-club connections in both functional and structural data. The ASD group exhibited higher connectivity in structural and functional networks but only inside the rich-club networks. These findings were replicated using the autism brain imaging data exchange (ABIDE) dataset with ASD (n = 85) and TD (n = 101). The ADHD group exhibited a lower generalized fractional anisotropy (GFA) and functional connectivity inside the rich-club networks, but a higher number of axonal fibers and correlation coefficient values outside the rich-club. Despite some shared biological features and frequent comorbity, these data suggest ADHD and ASD exhibit distinct large-scale connectivity patterns in middle childhood.
The lp-ntPET (“linear parametric ntPET”) model estimates time-variation in endogenous neurotransmitter levels from dynamic PET data. The pattern of dopamine change over time may be an important element of the brain’s response to addictive substances such as cigarettes or alcohol. We have extended the lp-ntPET model from the original ROI-based implementation to be able to apply the model at the voxel-level. The resulting endpoint is a dynamic image, or movie, of transient neurotransmitter changes. Simulations were performed to select threshold values to reduce the false positive rate when applied to real 11C-raclopride PET data. We tested the new voxel-wise method on simulated data and finally, we applied it to 11C-raclopride PET data of subjects smoking cigarettes in the PET scanner. In simulation, the temporal precision of neurotransmitter response was shown to be similar to that of ROI-based lp-ntPET (standard deviation ~3 min). False positive rates for the voxel-wise method were well controlled by combining a statistical threshold (the F-test) with a new spatial (cluster-size) thresholding operation. Sensitivity of detection for the new algorithm was greater than 80 % for the case of short-lived dopamine changes that occur in sub-regions of the striatum as might be the case with cigarette smoking. Finally, in 11C-raclopride PET data, dopamine movies reveal for the first time that different temporal patterns of the dopamine response to smoking may exist in different sub-regions of the striatum. These spatio-temporal patterns of neurotransmitter change created by voxel-wise lp-ntPET may serve as novel biomarkers for addiction and/or treatment efficacy.
lp-ntPET; time-varying parameters; dopamine; sensitivity; voxel analysis; nicotine
The basal ganglia (BG) mediate certain types of procedural learning, such as probabilistic classification learning on the ‘weather prediction task’ (WPT). Patients with Parkinson's disease (PD), who have BG dysfunction, are impaired at WPT-learning, but it remains unclear what component of the WPT is important for learning to occur. We tested the hypothesis that learning through processing of corrective feedback is the essential component and is associated with release of striatal dopamine. We employed two WPT paradigms, either involving learning via processing of corrective feedback (FB) or in a paired associate manner (PA). To test the prediction that learning on the FB but not PA paradigm would be associated with dopamine release in the striatum, we used serial 11C-raclopride (RAC) positron emission tomography (PET), to investigate striatal dopamine release during FB and PA WPT-learning in healthy individuals. Two groups, FB, (n = 7) and PA (n = 8), underwent RAC PET twice, once while performing the WPT and once during a control task. Based on a region-of-interest approach, striatal RAC-binding potentials reduced by 13–17% in the right ventral striatum when performing the FB compared to control task, indicating release of synaptic dopamine. In contrast, right ventral striatal RAC binding non-significantly increased by 9% during the PA task. While differences between the FB and PA versions of the WPT in effort and decision-making is also relevant, we conclude striatal dopamine is released during FB-based WPT-learning, implicating the striatum and its dopamine connections in mediating learning with FB.
basal ganglia; 11C-raclopride positron emission tomography; non-motor skill learning; probabilistic learning; procedural learning; weather prediction task
Differing imaging modalities provide unique channels of information to probe differing aspects of the brain’s structural or functional organization. In combination, differing modalities provide complementary and mutually informative data about tissue organization that is more than their sum. We acquired and spatially coregistered data in four MRI modalities – anatomical MRI, functional MRI, diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (MRS) – from 20 healthy adults to understand how inter-individual variability in measures from one modality account for variability in measures from other modalities at each voxel of the brain. We detected significant correlations of local volumes with the magnitude of functional activation, suggesting that underlying variation in local volumes contributes to individual variability in functional activation. We also detected significant inverse correlations of NAA (a putative measure of neuronal density and viability) with volumes of white matter in the frontal cortex, with DTI-based measures of tissue organization within the superior longitudinal fasciculus, and with the magnitude of functional activation and default-mode activity during simple visual and motor tasks, indicating that substantial variance in local volumes, white matter organization, and functional activation derives from an underlying variability in the number or density of neurons in those regions. Many of these imaging measures correlated with measures of intellectual ability within differing brain tissues and differing neural systems, demonstrating that the neural determinants of intellectual capacity involve numerous and disparate features of brain tissue organization, a conclusion that could be made with confidence only when imaging the same individuals with multiple MRI modalities.
multimodal MRI; anatomical MRI; functional MRI; diffusion tensor imaging; magnetic resonance spectroscopy; correlation; brain structure; brain function
Epileptic seizures can initiate a neural circuit and lead to aberrant neural communication with brain areas outside the epileptogenic region. We focus on interictal activity in focal temporal lobe epilepsy and evaluate functional connectivity differences that emerge as function of bilateral versus strictly unilateral epileptiform activity. We assess the strength of functional connectivity at rest between the ictal and non-ictal temporal lobes, in addition to whole brain connectivity with the ictal temporal lobe. Results revealed strong connectivity between the temporal lobes for both patient groups, but this did not vary as a function of unilateral versus bilateral interictal status. Both the left and right unilateral temporal lobe groups showed significant anti-correlated activity in regions outside the epileptogenic temporal lobe, primarily involving the contralateral (non-ictal/non-pathologic) hemisphere, with precuneus involvement prominent. The bilateral groups did not show this contralateral anti-correlated activity. This anti-correlated connectivity may represent a form of protective and adaptive inhibition, helping to constrain epileptiform activity to the pathologic temporal lobe. The absence of this activity in the bilateral groups may be indicative of flawed inhibitory mechanisms, helping to explain their more widespread epileptiform activity. Our data suggest that the location and build up of epilepsy networks in the brain are not truly random, and are not limited to the formation of strictly epileptogenic networks. Functional networks may develop to take advantage of the regulatory function of structures such as the precuneus to instantiate an anti-correlated network, generating protective cortico-cortico inhibition for the purpose of limiting seizure spread or epileptogenesis.
Epilepsy; Unilateral versus bilateral epileptiform activity; Connectivity; Resting-state; Cortical inhibition
Head movement during functional magnetic resonance imaging (fMRI) degrades data quality. The effects of small movements can be ameliorated during data post-processing, but data associated with severe movement is frequently discarded. In discarding these data, it is often assumed that head-movement is a source of random error, and that data can be discarded from subjects with severe movement without biasing the sample. We tested this assumption by examining whether head movement was related to task difficulty and cognitive status among persons with Multiple Sclerosis (MS). Thirty-four persons with MS were scanned while performing a working memory task with three levels of difficulty (the N-back task). Maximum movement (angle, shift) was estimated for each difficulty level. Cognitive status was assessed by combining performance on a working memory and processing speed task. An interaction was found between task difficulty and cognitive status (high vs. low cognitive ability): there was a linear increase in movement as task difficulty increased that was larger among subjects with lower cognitive ability. Analyses of the signal-to-noise ratio (SNR) confirmed that increases in movement degraded data quality. Similar, though far smaller, effects were found in a cohort of healthy control (HC) subjects. Therefore, discarding data with severe movement artifact may bias MS samples such that only those with less-severe cognitive impairment are included in the analyses. However, even if such data are not discarded outright, subjects who move more (MS and HC) will contribute less to the group-level results because of degraded SNR.
β-amyloid (Aβ), a feature of Alzheimer’s disease (AD) pathology, may precede reduced glucose metabolism and gray matter volume and cognitive decline in AD patients. Accumulation of Aβ, however, has been also reported in cognitively intact older people, although it remains unresolved whether and how Aβ deposition, glucose metabolism, and gray matter volume relate to one another in cognitively normal elderly. Fifty-two cognitively normal older adults underwent Pittsburgh Compound B positron emission tomography (PIB-PET), [18F]fluorodeoxyglucose (FDG) PET, and structural magnetic resonance (MRI) imaging to measure whole brain amyloid deposition, glucose metabolism, and gray matter volume, respectively. Covariance patterns of these measures in association with global amyloid burden measured by PIB index were extracted using principal component analysis-based multivariate methods. Higher global amyloid burden was associated with relative increases of amyloid deposition and glucose metabolism and relative decreases of gray matter volume in brain regions collectively known as the default mode network including the posterior cingulate/precuneus, lateral parietal cortices, and medial frontal cortex. Relative increases of amyloid deposition and glucose metabolism were also noted in the lateral prefrontal cortices, and relative decreases of gray matter volume were pronounced in hippocampus. The degree of expression of the topographical patterns of the PIB data was further associated with visual memory performance when controlling for age, sex, and education. The present findings suggest that cognitively normal older adults with greater amyloid deposition are relatively hypermetabolic in frontal and parietal brain regions while undergoing gray matter volume loss in overlapping brain regions.
beta-amyloid; PET; glucose metabolism; gray matter volume; aging
Depression is very common in multiple sclerosis (MS) but the underlying biological mechanisms are poorly understood. The hippocampus plays a key role in mood regulation and is implicated in the pathogenesis of depression. This study utilizes volumetric and shape analyses of the hippocampus to characterize neuroanatomical correlates of depression in MS. A cross-section of 109 female MS patients was evaluated. Bilateral hippocampi were segmented from MRI scans (volumetric T1-weighted, 1mm3) using automated tools. Shape analysis was performed using surface mesh modeling. Depression was assessed using the Center for Epidemiologic Studies-Depression (CES-D) scale. Eighty-three subjects were classified as low depression (CES-D 0-20) versus 26 subjects with high depression (CES-D ≥ 21). Right hippocampal volumes (p=0.04) were smaller in the high depression versus the low depression groups, but there was no significant difference in left hippocampal volumes. Surface rendering analysis revealed hippocampal shape changes in depressed MS patients were clustered in the right hippocampus. Significant associations were found between right hippocampal shape and affective symptoms but not vegetative symptoms of depression. Our results suggested that regionally clustered reductions in hippocampal thickness can be detected by automated surface mesh modeling and may be a biological substrate of MS depression in female patients.
depression; autoimmunity; hippocampus; cornu ammonis; magnetic resonance imaging
The timing and developmental factors underlying the establishment of language dominance are poorly understood. We investigated the degree of lateralization of traditional fronto-temporal and modulatory prefrontal-cerebellar regions of the distributed language network in children (n=57) ages 4 to 12 – a critical period for language consolidation. We examined the relationship between the strength of language lateralization and neuropsychological measures and task performance. The fundamental language network is established by four with ongoing maturation of language functions as evidenced by strengthening of lateralization in the traditional frontotemporal language regions; temporal regions were strongly and consistently lateralized by seven, while frontal regions had greater variability and were less strongly lateralized through age ten. In contrast, the modulatory prefrontal-cerebellar regions were the least strongly lateralized and degree of lateralization was not associated with age. Stronger core language skills were significantly correlated with greater right lateralization in the cerebellum.
Although blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) experiments of brain activity generally rely on the magnitude of the signal, they also provide frequency information that can be derived from the phase of the signal. However, because of confounding effects of instrumental and physiological origin, BOLD related frequency information is difficult to extract and therefore rarely used. Here, we explored the use of high field (7 T) and dedicated signal processing methods to extract frequency information and use it to quantify and interpret blood oxygenation and blood volume changes. We found that optimized preprocessing improves detection of task-evoked and spontaneous changes in phase signals and resonance frequency shifts over large areas of the cortex with sensitivity comparable to that of magnitude signals. Moreover, our results suggest the feasibility of mapping BOLD quantitative susceptibility changes in at least part of the activated area and its largest draining veins. Comparison with magnitude data suggests that the observed susceptibility changes originate from neuronal activity through induced blood volume and oxygenation changes in pial and intracortical veins. Further, from frequency shifts and susceptibility values, we estimated that, relative to baseline, the fractional oxygen saturation in large vessels increased by 0.02–0.05 during stimulation, which is consistent to previously published estimates. Together, these findings demonstrate that valuable information can be derived from fMRI imaging of BOLD frequency shifts and quantitative susceptibility changes.
BOLD fMRI phase signal changes; BOLD fMRI resonance frequency shifts; BOLD fMRI quantitative susceptibility changes; fractional oxygen saturation
In this paper, we present PLACE, a comprehensive framework for studying node-level community structure. Instead of the well-known Q modularity metric, PLACE utilizes a novel metric, ΨPL, which measures the difference between inter-community versus intra-community path lengths. We compared community structures in human healthy brain networks generated using these two metrics, and argued that ΨPL may have theoretical advantages. PLACE consists of the following: 1) extracting community structure using top-down hierarchical binary trees, where a branch at each bifurcation denotes a collection of nodes that form a community at that level, 2) constructing and assessing mean group community structure, and 3) detecting node-level changes in community between groups. We applied PLACE and investigated the structural brain networks obtained from a sample of 25 euthymic bipolar I subjects versus 25 gender and age matched healthy controls. Results showed community structural differences in posterior default mode network (DMN) regions, with the bipolar group exhibiting left-right decoupling.
connectome; community structure; bipolar disorder; hierarchical trees
This paper describes a novel approach to extract cortical morphological abnormality patterns from structural magnetic resonance imaging (MRI) data to improve the prediction accuracy of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Conventional approaches extract cortical morphological information, such as regional mean cortical thickness and regional cortical volumes, independently at different regions of interest (ROIs) without considering the relationship between these regions. Our approach involves constructing a similarity map where every element in thte map represents the correlation of regional mean cortical thickness between a pair of ROIs. We will demonstrate in this paper that this correlative morphological information gives significant improvement in classification performance when compared to ROI-based morphological information. Classification performance is further improved by integrating the correlative information with ROI-based information via multi-kernel support vector machines. This integrated framework achieves an accuracy of 92.35% for AD classification with an area of 0.9744 under the receiver operating characteristic (ROC) curve, and an accuracy of 83.75% for MCI classification with an area of 0.9233. In differentiating MCI subjects who converted to AD within 36 months from non-converters, an accuracy of 75.05% with an area of 0.8426 under ROC curve was achieved, indicating excellent diagnostic power and generalizability. The current work provides an alternative approach to extraction of high-order cortical information from structural MRI data for prediction of neurodegerative diseases such as AD.
Alzheimer’s disease (AD); mild cognitive impairment (MCI); magnetic resonance imaging (MRI), cortical thickness, multi-kernel support vector machine (SVM)
Posttraumatic stress disorder (PTSD) has a well-defined set of symptoms that can be elicited during traumatic imagery tasks. For this reason, trauma imagery tasks are often employed in functional neuroimaging studies. Here, coordinate-based meta-analysis (CBM) was used to pool eight studies applying traumatic imagery tasks to identify sites of task-induced activation in 170 PTSD patients and 104 healthy controls. In this way, right anterior cingulate (ACC), right posterior cingulate (PCC) and left precuneus (Pcun) were identified as regions uniquely active in PTSD patients relative to healthy controls. To further characterize these regions, their normal interactions, and their typical functional roles, meta-analytic connectivity modeling (MACM) with behavioral filtering was applied. MACM indicated that the PCC and Pcun regions were frequently co-active and associated with processing of cognitive information, particularly in explicit memory tasks. Emotional processing was particularly associated with co-activity of the ACC and PCC, as mediated by the thalamus. By narrowing the regions of interest to those commonly active across multiple studies (using CBM), and developing a priori hypotheses about directed probabilistic dependencies amongst these regions, this proposed model – when applied in the context of graphical and causal modeling – should improve model fit and thereby increase statistical power for detecting differences between subject groups and between treatments in neuroimaging studies of PTSD.
Posttraumatic stress disorder; meta-analysis; neuroimaging; trauma; imagery; connectivity
Advancing age results in altered cognitive and neuroimaging-derived markers of neural integrity. Whether cognitive changes are the result of variations in brain measures remains unclear and relating the two across the lifespan poses a unique set of problems. It must be determined whether statistical associations between cognitive and brain measures truly exist and are not epiphenomenal due solely to their shared relationships with age. The purpose of this study was to determine whether cerebral blood flow (CBF) and gray matter volume (GMV) measures make unique and better predictions of cognition than age alone. Multivariate analyses identified brain-wide covariance patterns from 35 healthy young and 23 healthy older adults using MRI-derived measures of CBF and GMV related to three cognitive composite scores (i.e., memory, fluid ability, and speed/attention). These brain-cognitive relationships were consistent across the age range, and not the result of epiphenomenal associations with age and each imaging modality provided its own unique information. The CBF and GMV patterns each accounted for unique aspects of cognition and accounted for nearly all the age-related variance in the cognitive composite scores. The findings suggest that measures derived from multiple imaging modalities explain larger amounts of variance in cognition providing a more complete understanding of the aging brain.
aging; multiple modality imaging; cognitive decline; cerebral blood flow; gray matter volume; multivariate analysis
A recently developed measure of structural brain connectivity disruption, the Loss in Connectivity (LoCo), is adapted for studies in alcohol dependence. LoCo uses independent tractography information from young healthy controls to project the location of white matter microstructure abnormalities in alcohol dependent vs. non-dependent individuals onto connected gray matter regions. The LoCo scores are computed from white matter abnormality masks derived at two levels: 1) group-wise differences of alcohol dependent individuals versus light drinking controls and 2) differences of the alcohol dependent individual versus the light drinking control group. LoCo scores based on group-wise white matter differences show that gray matter regions belonging to the extended brain reward system-network (BRS) have significantly higher LoCo (i.e., disconnectivity) than those not in this network (t = 2.18, p = 0.016). LoCo scores based on individuals’ white matter differences are also higher in BRS vs. non-BRS (t = 5.26, p = 3.92×10−6) of alcohol dependent individuals. These results suggest that white matter alterations in alcohol dependence, although subtle and spatially heterogeneous across the population, are nonetheless preferentially localized to the BRS. LoCo is shown to provide a more sensitive estimate of gray matter involvement than conventional volumetric gray matter measures, by differentiating better between brains of alcohol dependent individuals and non-alcoholic controls (rates of 89.3% versus 69.6%). However, just as volumetric measures, LoCo is not significantly correlated with standard drinking severity measures. LoCo is a sensitive white matter measure of regional cortical disconnectivity that uniquely characterizes anatomical network disruptions in alcohol dependence.
structural brain connectivity; tractography; white matter injury; alcohol dependence; addiction; brain reward system
Although it is inarguable that conventional MRI (cMRI) has greatly contributed to the diagnosis and assessment of Multiple Sclerosis (MS), cMRI does not show close correlation with clinical findings or pathologic features, and is unable to predict prognosis or stratify disease severity. To this end, diffusion tensor imaging (DTI) with tractography and neuroconnectivity analysis may assist disease assessment in MS. We therefore attempted this pilot study for initial assessment of early relapsing remitting (RR) MS. Neuroconnectivity analysis was employed for evaluation of 24 early RRMS patients within two years of presentation, and compared to the network measures of a group of 30 age-and-gender-matched normal control subjects. To account for the situation that the connections between two adjacent regions may be disrupted by an MS lesion, a new metric, network communicability, was adopted to measure both direct and indirect connections. For each anatomical area, the brain network communicability and average path length (APL) were computed and compared to characterize the network changes in efficiencies. Statistically significant (p < 0.05) loss of communicability was revealed in our RRMS cohort, particularly in the frontal and hippocampal/parahippocampal regions as well as the motor strip and occipital lobes. Correlation with the 25 foot walk test with communicability measures in the left superior frontal (r = -0.71) as well as the left superior temporal gyrus (r = -0.43) and left postcentral gyrus (r = -0.41) were identified. Additionally identified were increased communicability between the deep gray matter (GM) structures (left thalamus and putamen) with the major inter-hemispheric and intra-hemispheric white matter tracts, the corpus callosum and cingulum respectively. These foci of increased communicability are thought to represent compensatory changes. The proposed DTI based neuroconnectivity analysis demonstrated quantifiable, structurally relevant alterations of fiber tract connections in early relapsing remitting MS and paves the way for longitudinal studies in larger patient groups.
Lesion analysis is a classic approach to study brain functions. Because brain function is a result of coherent activations of a collection of functionally related voxels, lesion-symptom relations are generally contributed by multiple voxels simultaneously. Although voxel-based lesion symptom mapping (VLSM) has made substantial contributions to the understanding of brain-behavior relationships, a better understanding of the brain-behavior relationship contributed by multiple brain regions needs a multivariate lesion symptom mapping (MLSM). The purpose of this paper was to develop an MLSM using a machine learning-based multivariate regression algorithm: support vector regression (SVR). In the proposed SVR-LSM, the symptom relation to the entire lesion map as opposed to each isolated voxel is modeled using a non-linear function, so the inter-voxel correlations are intrinsically considered, resulting in a potentially more sensitive way to examine lesion-symptom relationships. To explore the relative merits of VLSM and SVR-LSM we used both approaches in the analysis of a synthetic dataset. SVR-LSM showed much higher sensitivity and specificity for detecting the synthetic lesion-behavior relations than VLSM. When applied to lesion data and language measures from patients with brain damages, SVR-LSM reproduced the essential pattern of previous findings identified by VLSM and showed higher sensitivity than VLSM for identifying the lesion-behavior relations. Our data also showed the possibility of using lesion data to predict continuous behavior scores.
Lesion-symptom mapping; support vector regression; aphasia; total lesion volume control
Individuals with a family history of substance use disorder (FH+) show impaired frontal white matter as indicated by diffusion tensor imaging (DTI). This impairment may be due to impaired or delayed development of myelin in frontal regions, potentially contributing to this population’s increased risk for developing substance use disorders. In this study, we examined high angular resolution DTI and proton magnetic resonance spectroscopy data from the anterior corona radiata were collected in 80 FH+ and 34 FH− youths (12.9 ±1.0 years old). White matter integrity indices included fractional anisotropy (FA), N-acetylaspartate (NAA), and total choline (tCho). Lower FA suggests decreased myelination. Decreased NAA coupled with higher tCho suggests impaired build-up and maintenance of cerebral myelin and consequently greater breakdown of cellular membranes. We found FH+ youths had lower FA (P <0.0001) and NAA (P =0.017) and higher tCho (P =0.04). FH density (number of parents and grandparents with substance use disorders) was negatively correlated with FA (P <0.0001) and NAA (P =0.011) and positively correlated with tCho (P =0.001). FA was independently predicted by both FH density (P =0.006) and NAA (P= 0.002), and NAA and tCho were both independent predictors of FH density (P <0.001). Our finding of lower frontal FA in FH+ youths corresponding to lower NAA and increased tCho is consistent with delayed or impaired development of frontal white matter in FH+ youths. Longitudinal studies are needed to determine how these differences relate to substance use outcomes.
frontal white matter integrity; family history; risk; diffusion tensor imaging; proton magnetic resonance spectroscopy; substance use
First impressions, especially of emotional faces, may critically impact later evaluation of social interactions. Activity in limbic regions, including the amygdala and ventral striatum, has previously been shown to correlate with identification of emotional content in faces; however, little work has been done describing how these signals may influence emotional face memory. We report an event-related fMRI study in 21 healthy adults where subjects attempted to recognize a neutral face that was previously viewed with a threatening (angry or fearful) or non-threatening (happy or sad) affect. In a hypothesis-driven region of interest analysis, we found that neutral faces previously presented with a threatening affect recruited the left amygdala. In contrast, faces previously presented with a non-threatening affect activated the left ventral striatum. A whole-brain analysis revealed increased response in the right orbitofrontal cortex to faces previously seen with threatening affect. These effects of prior emotion were independent of task performance, with differences being seen in the amygdala and ventral striatum even if only incorrect trials were considered. The results indicate that a network of frontolimbic regions may provide emotional bias signals during facial recognition.
amygdala; ventral striatum; fMRI; face; memory; emotion; orbitofrontal cortex
We characterized metabolic changes along the cortico-spinal tract (CST) in multiple sclerosis (MS) patients using a novel application of chemical shift imaging (CSI) and considering the spatial variation of metabolite levels. Thirteen relapsing-remitting (RR) and 13 primary-progressive (PP) MS patients and 16 controls underwent 1H-MR CSI, which was applied to coronal-oblique scans to sample the entire CST. The concentrations of the main metabolites, i.e., N-acetyl-aspartate, myo-Inositol (Ins), choline containing compounds (Cho) and creatine and phosphocreatine (Cr), were calculated within voxels placed in regions where the CST is located, from cerebral peduncle to corona radiata. Differences in metabolite concentrations between groups and associations between metabolite concentrations and disability were investigated, allowing for the spatial variability of metabolite concentrations in the statistical model. RRMS patients showed higher CST Cho concentration than controls, and higher CST Ins concentration than PPMS, suggesting greater inflammation and glial proliferation in the RR than in the PP course. In RRMS, a significant, albeit modest, association between greater Ins concentration and greater disability suggested that gliosis may be relevant to disability. In PPMS, lower CST Cho and Cr concentrations correlated with greater disability, suggesting that in the progressive stage of the disease, inflammation declines and energy metabolism reduces. Attention to the spatial variation of metabolite concentrations made it possible to detect in patients a greater increase in Cr concentration towards the superior voxels as compared to controls and a stronger association between Cho and disability, suggesting that this step improves our ability to identify clinically relevant metabolic changes.
multiple sclerosis; MRI; MRS
Functional connectivity examines temporal statistical dependencies among distant brain regions by means of seed-based analysis or independent component analysis (ICA). Spatial ICA also makes it possible to investigate functional connectivity at the network level, termed functional network connectivity (FNC). The dynamics of each network (ICA component) which may consist of several remote regions is described by the ICA time-course of that network; hence FNC studies statistical dependencies among ICA time-courses. In this paper, we compare comprehensively FNC in the resting state and during performance of an auditory oddball (AOD) task in 28 healthy subjects on relevant (non-artifactual) brain networks. The results show global FNC decrease during the performance of the task. Also, we show that specific networks enlarge and/or demonstrate higher activity during the performance of the task. The results suggest that performing an active task like AOD may be facilitated by recruiting more neurons and higher activation of related networks rather than collaboration among different brain networks. We also evaluated the impact of temporal filtering on FNC analyses. Results showed that the final results are not significantly affected by filtering.
fMRI; Functional network connectivity; Independent component analysis
Fetal alcohol spectrum disorders (FASD) are debilitating, with effects of prenatal alcohol exposure persisting into adolescence and adulthood. Complete characterization of FASD is crucial for the development of diagnostic tools and intervention techniques to decrease the high cost to individual families and society of this disorder. In this experiment we investigated visual system deficits in adolescents (12-21 years) diagnosed with an FASD by measuring the latency of patients’ primary visual M100 responses using MEG. We hypothesized that patients with FASD would demonstrate delayed primary visual responses compared to controls. M100 latencies were assessed both for FASD patients and age-matched healthy controls for stimuli presented at the fovea (central stimulus) and at the periphery (peripheral stimuli; left or right of the central stimulus) in a saccade task requiring participants to direct their attention and gaze to these stimuli. Source modeling was performed on visual responses to the central and peripheral stimuli and the latency of the first prominent peak (M100) in the occipital source timecourse was identified. The peak latency of the M100 responses were delayed in FASD patients for both stimulus types (central and peripheral), but the difference in latency of primary visual responses to central vs. peripheral stimuli was significant only in FASD patients, indicating that, while FASD patients’ visual systems are impaired in general, this impairment is more pronounced in the periphery. These results suggest that basic sensory deficits in this population may contribute to sensorimotor integration deficits described previously in this disorder.
Fetal Alcohol Spectrum Disorders; Magnetoencephalography; Vision; Occipital Cortex; M100; Primary Visual Cortex; Saccade
Cerebral white matter degeneration occurs with increasing age and is associated with declining cognitive function. Research has shown that cardiorespiratory fitness and exercise are effective as protective, even restorative, agents against cognitive and neurobiological impairments in older adults. In this study, we investigated whether the beneficial impact of aerobic fitness would extend to white matter integrity in the context of a one-year exercise intervention. Further, we examined the pattern of diffusivity changes to better understand the underlying biological mechanisms. Finally, we assessed whether training-induced changes in white matter integrity would be associated with improvements in cognitive performance independent of aerobic fitness gains. Results showed that aerobic fitness training did not affect group-level change in white matter integrity, executive function, or short-term memory, but that greater aerobic fitness derived from the walking program was associated with greater change in white matter integrity in the frontal and temporal lobes, and greater improvement in short-term memory. Increases in white matter integrity, however, were not associated with short-term memory improvement, independent of fitness improvements. Therefore, while not all findings are consistent with previous research, we provide novel evidence for correlated change in training-induced aerobic fitness, white matter integrity, and cognition among healthy older adults.
Diffusion tensor imaging; Anisotropy; Cerebrum; Cognition; Physical fitness; Aging
Although personality changes have been associated with brain lesions and atrophy caused by neurodegenerative diseases and aging, neuroanatomical correlates of personality in healthy individuals and their stability over time have received relatively little investigation. In this study, we explored regional gray matter (GM) volumetric associations of the five-factor model of personality. Eighty-seven healthy older adults took the NEO Personality Inventory and had brain MRI at two time points 2 years apart. We performed GM segmentation followed by regional analysis of volumes examined in normalized space map creation and voxel based morphometry-type statistical inference in SPM8. We created a regression model including all five factors and important covariates. Next, a conjunction analysis identified associations between personality scores and GM volumes that were replicable across time, also using cluster-level Family-Wise-Error correction. Larger right orbitofrontal and dorsolateral prefrontal cortices and rolandic operculum were associated with lower Neuroticism; larger left temporal, dorsolateral prefrontal, and anterior cingulate cortices with higher Extraversion; larger right frontopolar and smaller orbitofrontal and insular cortices with higher Openness; larger right orbitofrontal cortex with higher Agreeableness; larger dorsolateral prefrontal and smaller frontopolar cortices with higher Conscientiousness. In summary, distinct personality traits were associated with stable individual differences in GM volumes. As expected for higher-order traits, regions performing a large number of cognitive and affective functions were implicated. Our findings highlight personality-related variation that may be related to individual differences in brain structure that merit additional attention in neuroimaging research.
individual differences; trait; Neuroticism; Extraversion; Openness; Agreeableness; Conscientiousness; anterior cingulate; orbitofrontal cortex; frontopolar cortex