Sensory over-responsivity and social cognition in ASD: Effects of aversive sensory stimuli and attentional modulation on neural responses to social cues.

Sensory over-responsivity (SOR) is a common condition in autism spectrum disorders (ASD) that is associated with greater social impairment. However, the mechanisms through which sensory stimuli may affect social functioning are not well understood. This study used fMRI to examine brain activity while interpreting communicative intent in 15 high-functioning youth with ASD and 16 age- and IQ-matched typically-developing (TD) controls. Participants completed the task with and without a tactile sensory distracter, and with and without instructions directing their attention to relevant social cues. When completing the task in the presence of the sensory distracter, TD youth showed increased activity in auditory language and frontal regions whereas ASD youth showed decreased activation in these areas. Instructions mitigated this effect such that ASD youth did not decrease activation during tactile stimulation; instead, the ASD group showed increased medial prefrontal activity. SOR severity modulated the effect of the tactile stimulus on social processing. Results demonstrate for the first time a neural mechanism through which sensory stimuli cause disruption of social cognition, and that attentional modulation can restore neural processing of social cues through prefrontal regulation. Findings have implications for novel, integrative interventions that incorporate attentional directives to target both sensory and social symptoms

Dysfunctional Social Reinforcement Processing in Disruptive Behavior Disorders: An Functional Magnetic Resonance Imaging Study.

ObjectivePrior functional magnetic resonance imaging (fMRI) work has revealed that children/adolescents with disruptive behavior disorders (DBDs) show dysfunctional reward/non-reward processing of non-social reinforcements in the context of instrumental learning tasks. Neural responsiveness to social reinforcements during instrumental learning, despite the importance of this for socialization, has not yet been previously investigated.MethodsTwenty-nine healthy children/adolescents and 19 children/adolescents with DBDs performed the fMRI social/non-social reinforcement learning task. Participants responded to random fractal image stimuli and received social and non-social rewards/non-rewards according to their accuracy.ResultsChildren/adolescents with DBDs showed significantly reduced responses within the caudate and posterior cingulate cortex (PCC) to non-social (financial) rewards and social non-rewards (the distress of others). Connectivity analyses revealed that children/adolescents with DBDs have decreased positive functional connectivity between the ventral striatum (VST) and the ventromedial prefrontal cortex (vmPFC) seeds and the lateral frontal cortex in response to reward relative to non-reward, irrespective of its sociality. In addition, they showed decreased positive connectivity between the vmPFC seed and the amygdala in response to non-reward relative to reward.ConclusionThese data indicate compromised reinforcement processing of both non-social rewards and social non-rewards in children/adolescents with DBDs within core regions for instrumental learning and reinforcement-based decision- making (caudate and PCC). In addition, children/adolescents with DBDs show dysfunctional interactions between the VST, vmPFC, and lateral frontal cortex in response to rewarded instrumental actions potentially reflecting disruptions in attention to rewarded stimuli

Decoding Continuous Variables from Neuroimaging Data: Basic and Clinical Applications

The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods

Decoding Developmental Differences and Individual Variability in Response Inhibition Through Predictive Analyses Across Individuals

Response inhibition is thought to improve throughout childhood and into adulthood. Despite the relationship between age and the ability to stop ongoing behavior, questions remain regarding whether these age-related changes reflect improvements in response inhibition or in other factors that contribute to response performance variability. Functional neuroimaging data shows age-related changes in neural activity during response inhibition. While traditional methods of exploring neuroimaging data are limited to determining correlational relationships, newer methods can determine predictability and can begin to answer these questions. Therefore, the goal of the current study was to determine which aspects of neural function predict individual differences in age, inhibitory function, response speed, and response time variability. We administered a stop-signal task requiring rapid inhibition of ongoing motor responses to healthy participants aged 9–30. We conducted a standard analysis using GLM and a predictive analysis using high-dimensional regression methods. During successful response inhibition we found regions typically involved in motor control, such as the ACC and striatum, that were correlated with either age, response inhibition (as indexed by stop-signal reaction time; SSRT), response speed, or response time variability. However, when examining which variables neural data could predict, we found that age and SSRT, but not speed or variability of response execution, were predicted by neural activity during successful response inhibition. This predictive relationship provides novel evidence that developmental differences and individual differences in response inhibition are related specifically to inhibitory processes. More generally, this study demonstrates a new approach to identifying the neurocognitive bases of individual differences

Subregional Hippocampal Thickness Abnormalities in Older Adults with a History of Heavy Cannabis Use.

Background and Aims: Legalization of cannabis (CB) for both medicinal and, in some states, recreational use, has given rise to increasing usage rates across the country. Of particular concern are indications that frequent CB use may be selectively harmful to the developing adolescent brain compared with adult-onset usage. However, the long-term effects of heavy, adolescent CB use on brain structure and cognitive performance in late-life remain unknown. A critical brain region is the hippocampus (HC), where there is a striking intersection between high concentrations of cannabinoid 1 (CB1) receptors and age-related pathology. Design: We investigated whether older adults (average age=66.6+7.2 years old) with a history of early life CB use show morphological differences in hippocampal subregions compared with older, nonusers. Methods: We performed high-resolution magnetic resonance imaging combined with computational techniques to assess cortical thickness of the medial temporal lobe, neuropsychological testing, and extensive drug use histories on 50 subjects (24 formerly heavy cannabis users [CB+ group] abstinent for an average of 28.7 years, 26 nonusers [CB- group]). We investigated group differences in hippocampal subregions, controlling for age, sex, and intelligence (as measured by the Wechsler Test of Adult Reading), years of education, and cigarette use. Results: The CB+ subjects exhibited thinner cortices in subfields cornu ammonis 1 [CA1; F(1,42)=9.96, p=0.0003], and CA2, 3, and the dentate gyrus [CA23DG; F(1,42)=23.17, p<0.0001], and in the entire HC averaged over all subregions [F(1,42)=8.49, p=0.006]. Conclusions: Negative effects of chronic adolescent CB use on hippocampal structure are maintained well into late life. Because hippocampal cortical loss underlies and exacerbates age-related cognitive decline, these findings have profound implications for aging adults with a history of early life usage. Clinical Trial Registration: ClinicalTrials.gov # NCT01874886

Initial validation of a novel method of presurgical language localization through functional connectivity (fcMRI)

OBJECTIVE: Neurosurgery is potentially curative in chronic epilepsy but can only be offered to patients if the surgical risk to language is known. Clinical functional magnetic resonance imaging (fMRI) is an ideal, noninvasive method for localizing language cortex yet remains to be validated for this purpose. We have recently presented a novel method for localizing language cortex. Here we present a preliminary evaluation of this method’s validity. We hypothesized language regions identified using this novel method would demonstrate stronger functional connectivity than randomly generated set of proximal networks. METHOD: fMRI data were collected from sixteen temporal lobe patients (12 left) being evaluated for epilepsy surgery at UCLA (mean age 38.9 [sd 11.4]; 6 female; per Wada 14 left language dominant, 1 right, 1 mixed). Language maps were generated using a recently standardized method relying on a conjunction of language tasks (e.g., visual object naming; auditory naming; reading) to identify known language regions (Broca’s area; inferior and superior Wernicke’s Areas; Angular gyrus; Basal Temporal Language Area; Exner’s Area; and Supplementary Speech Area). With activations defined as network nodes, mean network connectivity was compared via permutation tests with alternate (i) fully random and (ii) proximal random networks. Mean network connectivity was determined in independently-acquired motor fMRI datasets (9 foot, 16 hand, 14 tongue). FINDINGS: 77% (30/39) of clinician-derived language networks exhibited mean connectivity greater than fully random networks (p\u3c0.05). Similarly, 69% (27/39) of clinician-derived language networks exhibited mean connectivity greater than proximal random networks (p\u3c0.05). Further analysis of networks not passing the permutation test suggests that low connectivity of non-valid networks may be driven not by low connectivity across all nodes, but by individual nodes that may not actually possess membership within the network. CONCLUSIONS: This study provides preliminary validity for a novel, clinician-based approach to mapping language cortex pre-surgery. This complements our recent work showing this method is reliable, and supports a proposed study comparing fMRI language maps using this technique with the results of direct stimulation mapping

Aging and brain activation with working memory tasks: An fMRI study of connectivity

SUMMARY Background White matter changes in aging and neuropsychiatric disorders may produce disconnection of neural circuits. Temporal correlations in regional blood oxygen level dependent (BOLD) signals may be used to assess effective functional connectivity in specific circuits, such as prefrontal cortex (PFC) circuits supporting working memory (WM) tasks. We hypothesized healthy older subjects would show lower connectivity than younger subjects. Methods Healthy younger (n ¼ 9, 25.9 (SD 6.0) years) and older adults (n ¼ 11, 68.3 (4.9) years) performed WM tasks during functional MRI. Subjects viewed images and were instructed to label them, either simultaneously or after a delay; BOLD responses with and without delay were contrasted to assess differential WM activation and connectivity. Two tasks were used: a semantic task, with line drawings categorized as 'alive' or 'not living', and an emotional task, with emotive faces as stimuli and subjects selecting the better emotional description. Results In both tasks, older subjects activated larger regions and had greater inter-individual variability in extent of activation. In the semantic task, connectivity was lower in the older subjects for the amygdala/orbital PFC circuit ( p ¼ 0.04). Contrary to our predictions, older subjects exhibited higher connectivity than younger subjects in the circuit linking orbital and dorsolateral PFC in both semantic ( p ¼ 0.04) and emotional ( p ¼ 0.02) tasks. Conclusions Healthy subjects exhibited age-dependent differences in connectivity in working memory circuits, but this may reflect effects of aging on white matter, compensatory mechanisms, and other factors. Volumetric determination of white matter hyperintensities in future studies may clarify the functional importance of structural damage