Annika C. Linke

Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML

Recent years have seen neuroimaging data sets becoming richer, with larger cohorts of participants, a greater variety of acquisition techniques, and increasingly complex analyses. These advances have made data analysis pipelines complicated to set up and run (increasing the risk of human error) and time consuming to execute (restricting what analyses are attempted). Here we present an open-source framework, automatic analysis (aa), to address these concerns. Human efficiency is increased by making code modular and reusable, and managing its execution with a processing engine that tracks what has been completed and what needs to be (re)done. Analysis is accelerated by optional parallel processing of independent tasks on cluster or cloud computing resources. A pipeline comprises a series of modules that each perform a specific task. The processing engine keeps track of the data, calculating a map of upstream and downstream dependencies for each module. Existing modules are available for many analysis tasks, such as SPM-based fMRI preprocessing, individual and group level statistics, voxel-based morphometry, tractography, and multi-voxel pattern analyses (MVPA). However, aa also allows for full customization, and encourages efficient management of code: new modules may be written with only a small code overhead. aa has been used by more than 50 researchers in hundreds of neuroimaging studies comprising thousands of subjects. It has been found to be robust, fast, and efficient, for simple-single subject studies up to multimodal pipelines on hundreds of subjects. It is attractive to both novice and experienced users. aa can reduce the amount of time neuroimaging laboratories spend performing analyses and reduce errors, expanding the range of scientific questions it is practical to address.

Stimulus-specific suppression preserves information in auditory short-term memory

Philosophers and scientists have puzzled for millennia over how perceptual information is stored in short-term memory. Some have suggested that early sensory representations are involved, but their precise role has remained unclear. The current study asks whether auditory cortex shows sustained frequency-specific activation while sounds are maintained in short-term memory using high-resolution functional MRI (fMRI). Investigating short-term memory representations within regions of human auditory cortex with fMRI has been difficult because of their small size and high anatomical variability between subjects. However, we overcame these constraints by using multivoxel pattern analysis. It clearly revealed frequency-specific activity during the encoding phase of a change detection task, and the degree of this frequency-specific activation was positively related to performance in the task. Although the sounds had to be maintained in memory, activity in auditory cortex was significantly suppressed. Strikingly, patterns of activity in this maintenance period correlated negatively with the patterns evoked by the same frequencies during encoding. Furthermore, individuals who used a rehearsal strategy to remember the sounds showed reduced frequency-specific suppression during the maintenance period. Although negative activations are often disregarded in fMRI research, our findings imply that decreases in blood oxygenation level-dependent response carry important stimulus-specific information and can be related to cognitive processes. We hypothesize that, during auditory change detection, frequency-specific suppression protects short-term memory representations from being overwritten by inhibiting the encoding of interfering sounds.

Encoding strategy accounts for individual differences in change detection measures of VSTM

Visual short-term memory (VSTM) capacity is often assessed using change detection tasks, and individual differences in performance have been shown to predict cognitive aptitudes across a range of domains in children and adults. We recently showed that intelligence correlates with an attentional component necessary for change detection rather than with memory capacity per se (Cusack, Lehmann, Veldsman, & Mitchell, 2009). It remained unclear, however, whether different attentional strategies during change detection have most impact during the encoding or maintenance of information. Here we present recent findings from our laboratory supporting the hypothesis that attentional selection during encoding dominates individual differences in change detection measures of visual short-term memory. In a first study, we unpredictably varied whether short-term memory was probed using change detection or whole report, encouraging participants to adopt the same encoding strategy throughout the tasks. Change detection performance of lower-IQ individuals improved. In a second study, we found that deficits in top-down attentional selectivity can be alleviated in participants with low change detection performance by providing helpful grouping information during encoding. Finally, a meta-analysis of neuroimaging data from 112 participants performing a variety of VSTM tasks showed that performance correlates with activity in several parietal and frontal regions during the encoding but not the maintenance phase. Taken together, these results support the notion that encoding strategy and not short-term memory capacity itself largely determines individual differences in visual change detection performance.

Flexible information coding in human auditory cortex during perception, imagery, and stm of complex sounds

Auditory cortex is the first cortical region of the human brain to process sounds. However, it has recently been shown that its neurons also fire in the absence of direct sensory input, during memory maintenance and imagery. This has commonly been taken to reflect neural coding of the same acoustic information as during the perception of sound. However, the results of the current study suggest that the type of information encoded in auditory cortex is highly flexible. During perception and memory maintenance, neural activity patterns are stimulus specific, reflecting individual sound properties. Auditory imagery of the same sounds evokes similar overall activity in auditory cortex as perception. However, during imagery abstracted, categorical information is encoded in the neural patterns, particularly when individuals are experiencing more vivid imagery. This highlights the necessity to move beyond traditional “brain mapping” inference in human neuroimaging, which assumes common regional activation implies similar mental representations.

Seeing different objects in different ways: Measuring ventral visual tuning to sensory and semantic features with dynamically adaptive imaging

A key challenge of object recognition is achieving a balance between selectivity for relevant features and invariance to irrelevant ones. Computational and cognitive models predict that optimal selectivity for features will differ by object, and here we investigate whether this is reflected in visual representations in the human ventral stream. We describe a new real‐time neuroimaging method, dynamically adaptive imaging (DAI), that enabled measurement of neural selectivity along multiple feature dimensions in the neighborhood of single referent objects. The neural response evoked by a referent was compared to that evoked by 91 naturalistic objects using multi‐voxel pattern analysis. Iteratively, the objects evoking the most similar responses were selected and presented again, to converge upon a subset that characterizes the referents “neural neighborhood.” This was used to derive the feature selectivity of the response. For three different referents, we found strikingly different selectivity, both in individual features and in the balance of tuning to sensory versus semantic features. Additional analyses placed a lower bound on the number of distinct activation patterns present. The results suggest that either the degree of specificity available for object representation in the ventral stream varies by class, or that different objects evoke different processing strategies. Hum Brain Mapp, 2012.

Optimizing stimulation and analysis protocols for neonatal fMRI.

The development of brain function in young infants is poorly understood. The core challenge is that infants have a limited behavioral repertoire through which brain function can be expressed. Neuroimaging with fMRI has great potential as a way of characterizing typical development, and detecting abnormal development early. But, a number of methodological challenges must first be tackled to improve the robustness and sensitivity of neonatal fMRI. A critical one of these, addressed here, is that the hemodynamic response function (HRF) in pre-term and term neonates differs from that in adults, which has a number of implications for fMRI. We created a realistic model of noise in fMRI data, using resting-state fMRI data from infants and adults, and then conducted simulations to assess the effect of HRF of the power of different stimulation protocols and analysis assumptions (HRF modeling). We found that neonatal fMRI is most powerful if block-durations are kept at the lower range of those typically used in adults (full on/off cycle duration 25-30s). Furthermore, we show that it is important to use the age-appropriate HRF during analysis, as mismatches can lead to reduced power or even inverted signal. Where the appropriate HRF is not known (for example due to potential developmental delay), a flexible basis set performs well, and allows accurate post-hoc estimation of the HRF.

Children with ASD show links between aberrant sound processing, social symptoms, and atypical auditory interhemispheric and thalamocortical functional connectivity

Highlights • Auditory processing deficits in ASD correlate with social and behavioral symptoms.• Reduced interhemispheric connectivity is found with more severe sensory symptoms.• Verbal IQ is lower in individuals with reduced interhemispheric connectivity.• Thalamocortical overconnectivity may compensate for sensory and social deficits.

Adult-like processing of naturalistic sounds in auditory cortex by 3- and 9-month old infants

&NA; Functional neuroimaging has been used to show that the developing auditory cortex of very young human infants responds, in some way, to sound. However, impoverished stimuli and uncontrolled designs have made it difficult to attribute brain responses to specific auditory features, and thus made it difficult to assess the maturity of feature tuning in auditory cortex. To address this, we used functional magnetic resonance imaging (fMRI) to measure the brain activity evoked by naturalistic sounds (a series of sung lullabies) in two groups of infants (3 and 9 months) and adults. We developed a novel analysis method – inter‐subject regression (ISR) – to quantify the similarity of cortical responses between infants and adults, and to decompose components of the response due to different auditory features. We found that the temporal pattern of activity in infant auditory cortex shared similarity with adults. Some of this shared response could be attributed to simple acoustic features, such as frequency, pitch, envelope, but other parts were not, suggesting that even more complex adult‐like features are represented in auditory cortex in early infancy. HighlightsComplex brain responses to naturalistic sounds were observed in 3‐month old infants.A novel method based on inter‐subject synchrony was used to tease these apart.Infant responses in auditory cortex were quantifiably similar to adult responses.Low‐level acoustic features could explain only a part of this common response.This suggests that complex adult‐like auditory processing is present at 3 months.

Psychotropic Medication Use in Autism Spectrum Disorders May Affect Functional Brain Connectivity

Background Prescription of psychotropic medications is common in autism spectrum disorders (ASDs), either off-label or to treat comorbid conditions such as ADHD or depression. Psychotropic medications are intended to alter brain function. Yet, studies investigating the functional brain organization in ASDs rarely take medication usage into account. This could explain some of the inconsistent findings of atypical brain network connectivity reported in the autism literature. Methods The current study tested whether functional connectivity patterns, as assessed with functional magnetic resonance imaging (fMRI), differed in a cohort of 49 children and adolescents with ASDs based on psychotropic medication status, and in comparison with 50 matched typically developing (TD) participants. Twenty-five participants in the ASD group (51%) reported current psychotropic medication usage, including stimulants, antidepressants, antipsychotics, and anxiolytics. Age, IQ, head motion, and ASD symptom severity did not differ between groups. Whole-brain functional connectivity between 132 regions of interest was assessed. Results Different functional connectivity patterns were identified in the ASD group taking psychotropic medications (ASD-on), as compared to the TD group and the ASD subgroup not using psychotropic medications (ASD-none). The ASD-on group showed distinct underconnectivity between the cerebellum and basal ganglia but cortico-cortical overconnectivity compared to the TD group. Cortical underconnectivity relative to the TD pattern, on the other hand, was pronounced in the ASD-none group. Conclusions These results suggest that psychotropic medications may affect functional connectivity, and that medication status should be taken into consideration when studying brain function in autism.

Repetitive behaviors in autism are linked to imbalance of corticostriatal connectivity: a functional connectivity MRI study

Abstract The neural underpinnings of repetitive behaviors (RBs) in autism spectrum disorders (ASDs), ranging from cognitive to motor characteristics, remain unknown. We assessed RB symptomatology in 50 ASD and 52 typically developing (TD) children and adolescents (ages 8–17 years), examining intrinsic functional connectivity (iFC) of corticostriatal circuitry, which is important for reward-based learning and integration of emotional, cognitive and motor processing, and considered impaired in ASDs. Connectivity analyses were performed for three functionally distinct striatal seeds (limbic, frontoparietal and motor). Functional connectivity with cortical regions of interest was assessed for corticostriatal circuit connectivity indices and ratios, testing the balance of connectivity between circuits. Results showed corticostriatal overconnectivity of limbic and frontoparietal seeds, but underconnectivity of motor seeds. Correlations with RBs were found for connectivity between the striatal motor seeds and cortical motor clusters from the whole-brain analysis, and for frontoparietal/limbic and motor/limbic connectivity ratios. Division of ASD participants into high (n = 17) and low RB subgroups (n = 19) showed reduced frontoparietal/limbic and motor/limbic circuit ratios for high RB compared to low RB and TD groups in the right hemisphere. Results suggest an association between RBs and an imbalance of corticostriatal iFC in ASD, being increased for limbic, but reduced for frontoparietal and motor circuits.