Faculty Bibliography

The functional MRI (fMRI) community has zealously embraced resting state or intrinsic functional connectivity approaches to mapping brain organization. Having demonstrated their utility for charting the large-scale functional architecture of the brain, the field is now leveraging task-independent methods for the investigation of phenotypic variation and the identification of biomarkers for clinical conditions. Enthusiasm aside, questions regarding the significance and validity of intrinsic brain phenomena remain. Here, we discuss these challenges and outline current developments that, in moving the field toward discovery science, permit a shift from cartography toward a mechanistic understanding of the neural bases of variation in cognition, emotion and behavior.

Idiopathic generalized epilepsy (IGE) is associated with widespread cortical network abnormalities on electroencephalography. Resting state functional connectivity (RSFC), based on fMRI, can assess the brain's global functional organization and its disruption in clinical conditions. We compared RSFC associated with the 'default mode network' (DMN) between people with IGE and healthy controls. Strength of functional connectivity within the DMN associated with seeds in the posterior cingulate cortex (PCC) and medial prefrontal cortices (MPFC) was compared between people with IGE and healthy controls and was correlated with seizure duration, age of seizure onset and age at scan. Those with IGE showed markedly reduced functional network connectivity between anterior and posterior cortical seed regions. Seizure duration positively correlates with RSFC between parahippocampal gyri and the PCC but negatively correlates with connectivity between the PCC and frontal lobe. The observed pattern of disruption provides evidence for integration- and segregation-type network abnormalities and supports aberrant network organization among people with IGE.

Abstract Objective. Brain iron deficiency has been supposed to be involved in the pathophysiology of ADHD. Available studies assessing iron in ADHD are based on serum ferritin, a peripheral marker of iron status. To what extent serum ferritin correlates with brain iron (BI) is unclear. The main aim of this study was to compare BI, estimated with magnetic resonance imaging (MRI) in the putamen, pallidum, caudate, and thalamus, between children with and without ADHD. The secondary aim was to assess the correlation between serum ferritin and BI levels. Methods. Thirty-six children (18 with and 18 without ADHD, the latter including nine healthy controls and nine psychiatric controls) completed MRI and blood sampling. Brain iron levels were estimated by imaging T2*. Results. Children with ADHD showed significantly lower estimated BI in right and left thalamus compared to healthy controls. Estimated BI did not differ significantly between children with ADHD and psychiatric controls. Children with ADHD had significantly lower levels of serum ferritin than healthy as well as psychiatric controls. Serum ferritin and T2* values did not correlate significantly in most regions. Conclusions. Low iron in the thalamus may contribute to ADHD pathophysiology.

Greater intra-subject variability (ISV) in response time is a heritable endophenotype of attention-deficit/hyperactivity disorder (ADHD). Spontaneous low frequency oscillations (LFO: 0.01-0.1 Hz) observed in brain functional magnetic resonance signals might account for such behavioral variability. Recently, we demonstrated that ISV in response time (RT) explained ratings of ADHD symptoms. Building on this finding, here we hypothesized that LFO in RT time series would explain these ratings, both independently and in addition to RT coefficient of variation (CV). To measure RT LFO, we applied Morlet wavelet transform to the previously collected RT data. Our community sample consisted of 98 children (including 66 boys, mean age 9.9 +/- 1.4 years), who completed four computer Tasks of Executive Control. Conners' Parent Rating Scale ratings were obtained. RT LFO of three tasks significantly explained ratings of inattention, hyperactivity and three global Conners' subscales. In addition, RT LFO during two tasks that included an inhibitory component increased the proportions of variance explained in subscales of both inattention and hyperactivity/impulsivity, beyond the effects of RT-CV. Three specific low frequency bands (Slow-5: 0.01-0.027 Hz; Slow-4: 0.027-0.073 Hz; Slow-3: 0.073-0.20 Hz) were strongly related to the ADHD scales. We conclude that RT LFO predict dimensional ratings of ADHD symptoms both independently and in addition to RTCV. Results suggest that frequency analyses are a suitable methodology to link behavioral responses to putative underlying physiological processes.

Attention-deficit/hyperactivity disorder (ADHD) has long been thought to reflect dysfunction of prefrontal-striatal circuitry, with involvement of other circuits largely ignored. Recent advances in systems neuroscience-based approaches to brain dysfunction have facilitated the development of models of ADHD pathophysiology that encompass a number of different large-scale resting-state networks. Here we review progress in delineating large-scale neural systems and illustrate their relevance to ADHD. We relate frontoparietal, dorsal attentional, motor, visual and default networks to the ADHD functional and structural literature. Insights emerging from mapping intrinsic brain connectivity networks provide a potentially mechanistic framework for an understanding of aspects of ADHD such as neuropsychological and behavioral inconsistency, and the possible role of primary visual cortex in attentional dysfunction in the disorder

Background and Objectives: Maltreatment from the caregiver induces vulnerability to later life psychopathology. Animal models of early life stress suggest this is due to disruption of neural development of long-distance circuits linking amygdala to prefrontal cortex. Methods: We used a rat model of early life maltreatment to examine amygdala connectivity using resting-state functional magnetic resonance imaging (R-fMRI). Rat pups were reared by a mother provided with insufficient bedding for nest building or by one with abundant bedding from postnatal days (PND) 8 to 12. In adolescence (at PND 45) and in early adulthood (at PND 60), R-fMRI sessions were conducted under light (*1%) isofluorane anesthesia. Behavioral tests were obtained in animals reared under identical conditions to model negative affectivity, including the Forced Swim Test, Sucrose Preference Test, and Social Behavior Test. Results: Behaviors reflecting negative affectivity were seen in both adolescent and adult animals. Amygdala functional connectivity (FC) with frontal, parietal, and basal ganglia, including thalamus, increased significantly with increased age. By contrast, local amygdala FC decreased significantly with age. Additionally, we detected significant interactions between abuse condition and age. Local amygdala FC decreased between PND 45 and 60 in control rats, but increased significantly in abused rats. The reverse pattern was observed for amygdala FC with medial frontal cortex and parietal cortex. Conclusions: Translation of an in vivo longitudinal imaging approach to a rodent model of early caregiver maltreatment revealed enduring evidence of differences in brain functional connectivity in adulthood that likely underlies negative affectivity and vulnerability to internalizing psychopathology in humans

Objectives: An important recent advance in echo planar imaging is the emergence of multiband echo planar imaging (MB-EPI, Moeller et al., 2010; Xu et al., 2012), which can provide low TRs or small voxel size to optimize temporal or spatial resolution for fMRI, respectively. The reliability of resting-state fMRI (R-fMRI) analyses performed on MB-EPI data has yet to be established. Here we address the test-retest (TRT) reliability of MB-EPI using intra-class correlation (ICC) for a variety of R-fMRI metrics, including: seed-based functional connectivity (FC, Biswal et al., (Figure psented) 1995), amplitude of low frequency fluctuations (ALFF, Zang et al., 2007), fractional amplitude of low frequency fluctuations (fALFF, Zou et al., 2008) and regional homogeneity (ReHo, Zang et al., 2004). Materials and Methods: We conducted an analysis of R-fMRI data from NKI-RS Multiband Imaging Test-Retest Pilot Dataset (http://fcon-1000.projects.nitrc.org) which consists of 2 scanning sessions separated by one week. The dataset includes: 1) MB-EPI/ TR = 645 (3mm isotropic voxels, 10-min scan), 2) MB-EPI/ TR = 1400 (2 mm, 10-min), and 3) a standard EPI sequence/ TR = 2500 (3 mm, 5-min) for each session. R-fMRI data were preprocessed and derivative maps were generated for full-length data (see Fig. 1 for details). To match scan length of the sequences, first 5-min data were also extract from preprocessed MB data for the ICC analysis. Results: All R-fMRI sequences demonstrated moderate to high TRT reliability across the brain for all measures from both fulllength, and 5-min data (Fig. 2 & 3); ICC values were most impressive for the MB-EPI/TR = 645 ms sequence, especially for seed-based FC and fALFF measures - attesting to the increased utility and reliability of this state-of-the-art sequence with unparalleled sampling rates for full brain acquisition. Conclusion: In summary, the pilot TRT reliability dataset suggests improved TRT reliability for the MB-EPI/TR = 645 ms sequence. The MB-EPI/TR = 1400 ms sequence offers improved spatial resolution with little or no cost of TRT reliability. Further testing will perform on voxel-mirrored homotopic connectivity, degree centrality, ICA as well as FC for other seed regions

Question: Verbal proficiency in 6 year-olds with Autistic Disorder (AD) is a prognostic factor of long-term functioning. As a first step toward identifying biomarkers as early as the first identification of AD, we characterize the neuronal underpinnings of verbal proficiency in children with AD. By means of resting-state fMRI (R-fMRI), we first examined intrinsic functional connectivity (iFC) of language-based circuits in a sample of school-age children. Then, to explore the stability of the identified marker(s), we examined its relationship with verbal proficiency in an independent group of preschoolers with AD. Methods: Two samples of children with AD included: 34 schoolage kids (age 11 +/- 2yrs) completing an awake R-fMRI scan; and 20 preschoolers (age 60 +/- 10months) completing a R-fMRI scan during natural sleep. To examine iFC of language circuits we focused on the left inferior frontal gyrus (IFG): the pars triangularis (pt), pars opercularis and ventral premotor cortex. We indexed verbal proficiency with the Vineland Expressive Language (VEL) standard scores of expressive language skills. In the 34 school-age children with AD, we examined the relationship between VEL scores and inter-individual differences in iFC patterns associated with each of the IFG seeds, at the voxel-wise, wholebrain level (Z > 2.3, p < 0.05, Gaussian random field theory corrected). Then, we examined the relationship between iFC of circuit( s) identified in the first step with the individual VEL score in the preschoolers with AD. We plan to apply one-class support vector machine to examine whether pattern of iFC can classify verbal proficient children with AD from those with poor verbal proficiency. Results: Voxel-wise analyses showed a significant positive relationship between VEL scores and the iFC between left IFGpt and a cluster in the posterior aspects of the right superior temporal sulcus (STS) in the school-age kids.Guided by this finding, we correlated the iFC within this circuit with VEL scores of 20 preschoolers with AD.The iFC of this circuit explained 16% of the variance in verbal proficiency (r = 0.40). Conclusions: R-fMRI during natural sleep provides a feasible means for identifying loci of disconnection in autism that may serve to identify prognostic markers of verbal proficiency at the individual level at the time of first diagnosis

Introduction: Recent work has demonstrated head motion contributes to artifactual differences in resting-state fMRI (R-fMRI) measures (Power et al., 2012a;Satterthwaite et al., 2012;Van Dijk et al., 2012). Here we explored how a broad array of R-fMRIbased intrinsic brain function measures are affected by head motion, and how such sensitivities and their test-retest (TRT) reliabilities are impacted by various motion correction strategies. Methods: After preprocessing publicly released developmental, young adult and TRT datasets, the following strategies were applied to correct head motion effects: regressing out 6 head motion parameters (Traditional 6), regressing out autoregressive models (Friston et al., 1996) (Friston 24), regressing out voxelspecific head motion regressors (Voxel-Specific 12), and data scrubbing at framewise displacement (FD) > 0.2 or 0.5mm. We then explored head motion effects and TRT reliability on amplitude of low frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity, voxel-mirrored homotopic connectivity, and functional connectivity of medial prefrontal cortex. Results: As previously suggested, head motion effects are stronger in developmental than adult data (Fig. 1 vs. Fig. 2). Among the measures, fALFF is least affected by head motion. Among head motion correction strategies, scrubbing at FD > 0.2 mm (Power et al., 2012b) cleared the most motion effect while creating artificial head motion effect in fALFF due to destruction of temporal structure. Scrubbing at FD > 0.2mm also diminished TRT reliability dramatically (Fig. 3); some subjects varied markedly in the number of time points excluded across sessions (e.g., (Figure Presented) 150 vs. 37). Importantly, head motion effects remained after all correction strategies (Figs. 1, 2) suggesting taking subject head motion into account at the group level is still necessary. Regressing out mean FD slightly decreased TRT reliability but preserved its structure (Fig. 4). Conclusion: Results suggest that head motion effects extend to all metrics when studying hyperkinetic populations. We suggest caution when using stringent scrubbing (e.g. FD > 0.2mm as recommend by Power et al. 2012b), as test-retest reliability can be compromised and frequency metrics made immeasurable. Correction for inter-individual differences in motion at the grouplevel appears to be necessary regardless of individual subject correction strategy

In recent years, there has been growing enthusiasm that functional magnetic resonance imaging (MRI) could achieve clinical utility for a broad range of neuropsychiatric disorders. However, several barriers remain. For example, the acquisition of large-scale datasets capable of clarifying the marked heterogeneity that exists in psychiatric illnesses will need to be realized. In addition, there continues to be a need for the development of image processing and analysis methods capable of separating signal from artifact. As a prototypical hyperkinetic disorder, and movement-related artifact being a significant confound in functional imaging studies, ADHD offers a unique challenge. As part of the ADHD-200 Global Competition and this special edition of Frontiers, the ADHD-200 Consortium demonstrates the utility of an aggregate dataset pooled across five institutions in addressing these challenges. The work aimed to (1) examine the impact of emerging techniques for controlling for "micro-movements," and (2) provide novel insights into the neural correlates of ADHD subtypes. Using support vector machine (SVM)-based multivariate pattern analysis (MVPA) we show that functional connectivity patterns in individuals are capable of differentiating the two most prominent ADHD subtypes. The application of graph-theory revealed that the Combined (ADHD-C) and Inattentive (ADHD-I) subtypes demonstrated some overlapping (particularly sensorimotor systems), but unique patterns of atypical connectivity. For ADHD-C, atypical connectivity was prominent in midline default network components, as well as insular cortex; in contrast, the ADHD-I group exhibited atypical patterns within the dlPFC regions and cerebellum. Systematic motion-related artifact was noted, and highlighted the need for stringent motion correction. Findings reported were robust to the specific motion correction strategy employed. These data suggest that resting-state functional connectivity MRI (rs-fcMRI) data can be used to characterize individual patients with ADHD and to identify neural distinctions underlying the clinical heterogeneity of ADHD.