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Peters J.M.,Computational Radiology Laboratory | Taquet M.,Computational Radiology Laboratory | Taquet M.,Catholic University of Louvain | Vega C.,Boston Childrens Hospital | And 7 more authors.
BMC Medicine | Year: 2013

Background: Graph theory has been recently introduced to characterize complex brain networks, making it highly suitable to investigate altered connectivity in neurologic disorders. A current model proposes autism spectrum disorder (ASD) as a developmental disconnection syndrome, supported by converging evidence in both non-syndromic and syndromic ASD. However, the effects of abnormal connectivity on network properties have not been well studied, particularly in syndromic ASD. To close this gap, brain functional networks of electroencephalographic (EEG) connectivity were studied through graph measures in patients with Tuberous Sclerosis Complex (TSC), a disorder with a high prevalence of ASD, as well as in patients with non-syndromic ASD.Methods: EEG data were collected from TSC patients with ASD (n = 14) and without ASD (n = 29), from patients with non-syndromic ASD (n = 16), and from controls (n = 46). First, EEG connectivity was characterized by the mean coherence, the ratio of inter- over intra-hemispheric coherence and the ratio of long- over short-range coherence. Next, graph measures of the functional networks were computed and a resilience analysis was conducted. To distinguish effects related to ASD from those related to TSC, a two-way analysis of covariance (ANCOVA) was applied, using age as a covariate.Results: Analysis of network properties revealed differences specific to TSC and ASD, and these differences were very consistent across subgroups. In TSC, both with and without a concurrent diagnosis of ASD, mean coherence, global efficiency, and clustering coefficient were decreased and the average path length was increased. These findings indicate an altered network topology. In ASD, both with and without a concurrent diagnosis of TSC, decreased long- over short-range coherence and markedly increased network resilience were found.Conclusions: The altered network topology in TSC represents a functional correlate of structural abnormalities and may play a role in the pathogenesis of neurological deficits. The increased resilience in ASD may reflect an excessively degenerate network with local overconnection and decreased functional specialization. This joint study of TSC and ASD networks provides a unique window to common neurobiological mechanisms in autism. © 2013 Peters et al; licensee BioMed Central Ltd. Source

Langer N.,University of Zurich | Langer N.,Laboratories of Cognitive Neuroscience | Langer N.,Harvard University | von Bastian C.C.,University of Zurich | And 5 more authors.
Cortex | Year: 2013

The human brain is a highly interconnected network. Recent studies have shown that the functional and anatomical features of this network are organized in an efficient small-world manner that confers high efficiency of information processing at relatively low connection cost. However, it has been unclear how the architecture of functional brain networks is related to performance in working memory (WM) tasks and if these networks can be modified by WM training. Therefore, we conducted a double-blind training study enrolling 66 young adults. Half of the subjects practiced three WM tasks and were compared to an active control group practicing three tasks with low WM demand. High-density resting-state electroencephalography (EEG) was recorded before and after training to analyze graph-theoretical functional network characteristics at an intracortical level. WM performance was uniquely correlated with power in the theta frequency, and theta power was increased by WM training. Moreover, the better a person's WM performance, the more their network exhibited small-world topology. WM training shifted network characteristics in the direction of high performers, showing increased small-worldness within a distributed fronto-parietal network. Taken together, this is the first longitudinal study that provides evidence for the plasticity of the functional brain network underlying WM. © 2013 Elsevier Ltd. Source

Langer N.,University of Zurich | Langer N.,Laboratories of Cognitive Neuroscience | Langer N.,Harvard University | Pedroni A.,University of Basel | And 2 more authors.
PLoS ONE | Year: 2013

Graph theory deterministically models networks as sets of vertices, which are linked by connections. Such mathematical representation of networks, called graphs are increasingly used in neuroscience to model functional brain networks. It was shown that many forms of structural and functional brain networks have small-world characteristics, thus, constitute networks of dense local and highly effective distal information processing. Motivated by a previous small-world connectivity analysis of resting EEG-data we explored implications of a commonly used analysis approach. This common course of analysis is to compare small-world characteristics between two groups using classical inferential statistics. This however, becomes problematic when using measures of inter-subject correlations, as it is the case in commonly used brain imaging methods such as structural and diffusion tensor imaging with the exception of fibre tracking. Since for each voxel, or region there is only one data point, a measure of connectivity can only be computed for a group. To empirically determine an adequate small-world network threshold and to generate the necessary distribution of measures for classical inferential statistics, samples are generated by thresholding the networks on the group level over a range of thresholds. We believe that there are mainly two problems with this approach. First, the number of thresholded networks is arbitrary. Second, the obtained thresholded networks are not independent samples. Both issues become problematic when using commonly applied parametric statistical tests. Here, we demonstrate potential consequences of the number of thresholds and non-independency of samples in two examples (using artificial data and EEG data). Consequently alternative approaches are presented, which overcome these methodological issues. © 2013 Langer et al. Source

Jeste S.S.,University of California at Los Angeles | Wu J.Y.,University of California at Los Angeles | Senturk D.,University of California at Los Angeles | Varcin K.,Laboratories of Cognitive Neuroscience | And 6 more authors.
Neurology | Year: 2014

Objective: We performed a longitudinal cohort study of infants with tuberous sclerosis complex (TSC), with the overarching goal of defining early clinical, behavioral, and biological markers of autism spectrum disorder (ASD) in this high-risk population. Methods: Infants with TSC and typically developing controls were recruited as early as 3 months of age and followed longitudinally until 36 months of age. Data gathered at each time point included detailed seizure history, developmental testing using the Mullen Scales of Early Learning, and socialcommunication assessments using the Autism Observation Scale for Infants. At 18 to 36 months, a diagnostic evaluation for ASD was performed using the Autism Diagnostic Observation Schedule. Results: Infants with TSC demonstrated delays confined to nonverbal abilities, particularly in the visual domain, which then generalized to more global delays by age 9 months. Twenty-two of 40 infants with TSC were diagnosed with ASD. Both 12-month cognitive ability and developmental trajectories over the second and third years of life differentiated the groups. By 12 months of age, the ASD group demonstrated significantly greater cognitive delays and a significant decline in nonverbal IQ from 12 to 36 months. Conclusions: This prospective study characterizes early developmental markers of ASD in infants with TSC. The early delay in visual reception and fine motor ability in the TSC group as a whole, coupled with the decline in nonverbal ability in infants diagnosed with ASD, suggests a domainspecific pathway to ASD that can inform more targeted interventions for these high-risk infants. © 2014 American Academy of Neurology. Source

Keehn B.,Laboratories of Cognitive Neuroscience | Keehn B.,Harvard University | Wagner J.,CUNY - College of Staten Island | Tager-Flusberg H.,Boston University | And 2 more authors.
Frontiers in Human Neuroscience | Year: 2013

Background: Autism spectrum disorder (ASD) has been called a 'developmental disconnection syndrome,' however the majority of the research examining connectivity in ASD has been conducted exclusively with older children and adults. Yet, prior ASD research suggests that perturbations in neurodevelopmental trajectories begin as early as the first year of life. Prospective longitudinal studies of infants at risk for ASD may provide a window into the emergence of these aberrant patterns of connectivity. The current study employed functional connectivity nearinfrared spectroscopy (NIRS) in order to examine the development of intra- and inter-hemispheric functional connectivity in high- and low-risk infants across the first year of life. Methods: NIRS data were collected from 27 infants at high risk for autism (HRA) and 37 low-risk comparison (LRC) infants who contributed a total of 116 data sets at 3-, 6-, 9-, and 12-months. At each time point, HRA and LRC groups were matched on age, sex, head circumference, and Mullen Scales of Early Learning scores. Regions of interest (ROI) were selected from anterior and posterior locations of each hemisphere. The average time course for each ROI was calculated and correlations for each ROI pair were computed. Differences in functional connectivity were examined in a cross-sectional manner. Results: At 3-months, HRA infants showed increased overall functional connectivity compared to LRC infants. This was the result of increased connectivity for intra- and inter-hemispheric ROI pairs. No significant differences were found between HRA and LRC infants at 6- and 9-months. However, by 12-months, HRA infants showed decreased connectivity relative to LRC infants. Conclusions: Our preliminary results suggest that atypical functional connectivity may exist within the first year of life in HRA infants, providing support to the growing body of evidence that aberrant patterns of connectivity may be a potential endophenotype for ASD. © 2013 Keehn, Wagner, Tager-flusberg and Nelson. Source

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