John Suckling

A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs

Small-world properties have been demonstrated for many complex networks. Here, we applied the discrete wavelet transform to functional magnetic resonance imaging (fMRI) time series, acquired from healthy volunteers in the resting state, to estimate frequency-dependent correlation matrices characterizing functional connectivity between 90 cortical and subcortical regions. After thresholding the wavelet correlation matrices to create undirected graphs of brain functional networks, we found a small-world topology of sparse connections most salient in the low-frequency interval 0.03–0.06 Hz. Global mean path length (2.49) was approximately equivalent to a comparable random network, whereas clustering (0.53) was two times greater; similar parameters have been reported for the network of anatomical connections in the macaque cortex. The human functional network was dominated by a neocortical core of highly connected hubs and had an exponentially truncated power law degree distribution. Hubs included recently evolved regions of the heteromodal association cortex, with long-distance connections to other regions, and more cliquishly connected regions of the unimodal association and primary cortices; paralimbic and limbic regions were topologically more peripheral. The network was more resilient to targeted attack on its hubs than a comparable scale-free network, but about equally resilient to random error. We conclude that correlated, low-frequency oscillations in human fMRI data have a small-world architecture that probably reflects underlying anatomical connectivity of the cortex. Because the major hubs of this network are critical for cognition, its slow dynamics could provide a physiological substrate for segregated and distributed information processing.

Neuroanatomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI comparison

BACKGROUND Psychotic disorders, such as schizophrenia, are associated with neuroanatomical abnormalities, but whether these predate the onset of symptoms or develop progressively over the course of illness is unclear. We investigated this issue with MRI to study people with prodromal symptoms who are at ultra high-risk for the development of psychosis. METHODS We did two comparisons, cross-sectional and longitudinal. For the cross-sectional comparison, 75 people with prodromal signs of psychosis were scanned with MRI. After at least 12 months of follow-up, 23 (31%) had developed psychosis and 52 (69%) had not. Baseline MRI data from these two subgroups were compared. For the longitudinal comparison, 21 of the ultra high-risk individuals were scanned again with MRI after at least 12 months. Ten of these had developed psychosis and 11 had not. MRI data from baseline and follow-up were compared within each group of people. FINDINGS In the cross-sectional comparison, compared with people who did not develop psychosis, those who did develop the disorder had less grey matter in the right medial temporal, lateral temporal, and inferior frontal cortex, and in the cingulate cortex bilaterally. In the longitudinal comparison, when re-scanned, individuals who had developed psychosis showed a reduction in grey matter in the left parahippocampal, fusiform, orbitofrontal and cerebellar cortices, and the cingulate gyri. In those who had not become psychotic, longitudinal changes were restricted to the cerebellum. INTERPRETATION Some of the grey-matter abnormalities associated with psychotic disorders predate the onset of frank symptoms, whereas others appear in association with their first expression.

Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain

The authors describe almost entirely automated procedures for estimation of global, voxel, and cluster-level statistics to test the null hypothesis of zero neuroanatomical difference between two groups of structural magnetic resonance imaging (MRI) data. Theoretical distributions under the null hypothesis are available for (1) global tissue class volumes; (2) standardized linear model [analysis of variance (ANOVA and ANCOVA)] coefficients estimated at each voxel; and (3) an area of spatially connected clusters generated by applying an arbitrary threshold to a two-dimensional (2-D) map of normal statistics at voxel level. The authors describe novel methods for economically ascertaining probability distributions under the null hypothesis, with fewer assumptions, by permutation of the observed data. Nominal Type I error control by permutation testing is generally excellent; whereas theoretical distributions may be over conservative. Permutation has the additional advantage that it can be used to test any statistic of interest, such as the sum of suprathreshold voxel statistics in a cluster (or cluster mass), regardless of its theoretical tractability under the null hypothesis. These issues are illustrated by application to MRI data acquired from 18 adolescents with hyperkinetic disorder and 16 control subjects matched for age and gender.

Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains.

Even in the absence of an experimental effect, functional magnetic resonance imaging (fMRI) time series generally demonstrate serial dependence. This colored noise or endogenous autocorrelation typically has disproportionate spectral power at low frequencies, i.e., its spectrum is  f–1 ‐like. Various pre‐whitening and pre‐coloring strategies have been proposed to make valid inference on standardised test statistics estimated by time series regression in this context of residually autocorrelated errors. Here we introduce a new method based on random permutation after orthogonal transformation of the observed time series to the wavelet domain. This scheme exploits the general whitening or decorrelating property of the discrete wavelet transform and is implemented using a Daubechies wavelet with four vanishing moments to ensure exchangeability of wavelet coefficients within each scale of decomposition. For  f–1 ‐like or fractal noises, e.g., realisations of fractional Brownian motion (fBm) parameterised by Hurst exponent 0 < H < 1, this resampling algorithm exactly preserves wavelet‐based estimates of the second order stochastic properties of the (possibly nonstationary) time series. Performance of the method is assessed empirically using  f–1 ‐like noise simulated by multiple physical relaxation processes, and experimental fMRI data. Nominal type 1 error control in brain activation mapping is demonstrated by analysis of 13 images acquired under null or resting conditions. Compared to autoregressive pre‐whitening methods for computational inference, a key advantage of wavelet resampling seems to be its robustness in activation mapping of experimental fMRI data acquired at 3 Tesla field strength. We conclude that wavelet resampling may be a generally useful method for inference on naturally complex time series. Hum. Brain Mapping 12:61–78, 2001.

Orbitofrontal dysfunction in patients with obsessive-compulsive disorder and their unaffected relatives

Obsessive-compulsive disorder (OCD) is characterized by repetitive thoughts and behaviors associated with underlying dysregulation of frontostriatal circuitry. Central to neurobiological models of OCD is the orbitofrontal cortex, a neural region that facilitates behavioral flexibility after negative feedback (reversal learning). We identified abnormally reduced activation of several cortical regions, including the lateral orbitofrontal cortex, during reversal learning in OCD patients and their clinically unaffected close relatives, supporting the existence of an underlying previously undiscovered endophenotype for this disorder.

Undirected graphs of frequency-dependent functional connectivity in whole brain networks

We explored properties of whole brain networks based on multivariate spectral analysis of human functional magnetic resonance imaging (fMRI) time-series measured in 90 cortical and subcortical subregions in each of five healthy volunteers studied in the (no-task) resting state. We note that undirected graphs representing conditional independence between multivariate time-series can be more readily approached in the frequency domain than the time domain. Estimators of partial coherency and normalized partial mutual information φ, an integrated measure of partial coherence over an arbitrary frequency band, are applied. Using these tools, we replicate the prior observations that bilaterally homologous brain regions tend to be strongly connected and functional connectivity is generally greater at low frequencies [0.0004, 0.1518 Hz]. We also show that long-distance intrahemispheric connections between regions of prefrontal and parietal cortex were more salient at low frequencies than at frequencies greater than 0.3 Hz, whereas many local or short-distance connections, such as those comprising segregated dorsal and ventral paths in posterior cortex, were also represented in the graph of high-frequency connectivity. We conclude that the partial coherency spectrum between a pair of human brain regional fMRI time-series depends on the anatomical distance between regions: long-distance (greater than 7 cm) edges represent conditional dependence between bilaterally symmetric neocortical regions, and between regions of prefrontal and parietal association cortex in the same hemisphere, are predominantly subtended by low-frequency components.

A meta-analysis of sex differences in human brain structure

Highlights • This is the first meta-analysis of sex differences in the typical human brain.• Regional sex differences overlap with areas implicated in psychiatric conditions.• The amygdala, hippocampus, planum temporale and insula display sex differences.• On average, males have larger brain volumes than females.• Most articles providing sex differences in volume are in the ‘mature’ category.

A quantitative meta-analysis of fMRI studies in bipolar disorder

OBJECTIVES Functional magnetic resonance imaging (fMRI) has been widely used to identify state and trait markers of brain abnormalities associated with bipolar disorder (BD). However, the primary literature is composed of small-to-medium-sized studies, using diverse activation paradigms on variously characterized patient groups, which can be difficult to synthesize into a coherent account. This review aimed to synthesize current evidence from fMRI studies in midlife adults with BD and to investigate whether there is support for the theoretical models of the disorder. METHODS We used voxel-based quantitative meta-analytic methods to combine primary data on anatomical coordinates of activation from 65 fMRI studies comparing normal volunteers (n = 1,074) and patients with BD (n = 1,040). RESULTS Compared to normal volunteers, patients with BD underactivated the inferior frontal cortex (IFG) and putamen and overactivated limbic areas, including medial temporal structures (parahippocampal gyrus, hippocampus, and amygdala) and basal ganglia. Dividing studies into those using emotional and cognitive paradigms demonstrated that the IFG abnormalities were manifest during both cognitive and emotional processing, while increased limbic activation was mainly related to emotional processing. In further separate comparisons between healthy volunteers and patient subgroups in each clinical state, the IFG was underactive in manic but not in euthymic and depressed states. Limbic structures were not overactive in association with mood states, with the exception of increased amygdala activation in euthymic states when including region-of-interest studies. CONCLUSIONS In summary, our results showed abnormal frontal-limbic activation in BD. There was attenuated activation of the IFG or ventrolateral prefrontal cortex, which was consistent across emotional and cognitive tasks and particularly related to the state of mania, and enhanced limbic activation, which was elicited by emotional and not cognitive tasks, and not clearly related to mood states.

Evaluating the effect of rectal distension and rectal movement on prostate gland position using cine MRI

PURPOSE To evaluate the dynamic interrelationship between rectal distension and rectal movements, and to determine the effect of rectal movement on the position of the prostatic gland using cine magnetic resonance imaging (MRI). METHODS AND MATERIALS Fifty-five patients with biopsy-proven or suspected prostate cancer were examined in the axial plane using repeated spoiled gradient-echo sequences every 10 seconds for 7 minutes. Twenty-four patients received bowel relaxants before imaging. Images were analyzed for the degree of rectal distension, for the incidence, magnitude, and number of rectal and prostate movements. RESULTS Rectal movements were seen in 28 (51%) patients overall, in 10 (42%) of those receiving bowel relaxants and in 18 (58%) not receiving bowel relaxants. The incidence of rectal movements correlated with the degree of rectal distension (p = 0.0005), but the magnitude of rectal movements did not correlate with the degree of rectal distension. Eighty-six rectal movements resulting in 33 anterior-posterior (AP) prostate movements were seen. The magnitude of rectal movements correlated well with degree of prostate movements (p < 0.001). Prostate movements in the AP direction were seen in 16 (29%) patients, and in 9 (16%) patients the movement was greater than 5 mm. The median prostate AP displacement was anterior by 4.2 (-5 to +14 mm). CONCLUSIONS Cine MRI is able to demonstrate near real time rectal and associated prostate movements. Rectal movements are related to rectal distension and result in significant displacements of the prostate gland over a time period similar to that used for daily fractionated radiotherapy treatments. Delivery of radiotherapy needs to take into account these organ movements.

Neural responses to sad facial expressions in major depression following cognitive behavioral therapy

BACKGROUND Affective facial processing is an important component of interpersonal relationships. The neural substrate has been examined following treatment with antidepressant medication but not with psychological therapies. The present study investigated the neural correlates of implicit processing of sad facial expressions in depression pretreatment and posttreatment with cognitive behavioral therapy (CBT). METHODS The patient group consisted of 16 medication-free subjects (mean age 40 years) with a DSM-IV diagnosis of acute unipolar major depression, and the comparison group were 16 matched healthy volunteers. Subjects participated in a prospective study with functional magnetic resonance imaging (fMRI) at weeks 0 and 16. During the fMRI scans, subjects performed an affect recognition task with facial stimuli morphed to display varying intensities of sadness. Patients received 16 sessions of CBT. Functional magnetic resonance imaging data were analyzed for the mean activation and differential response to variable intensity (load-response) of facial affect processing. RESULTS During an acute depressive episode, patients showed elevated amygdala-hippocampal activity relative to healthy individuals. Baseline dorsal anterior cingulate activity in patients showed a significant relationship with subsequent clinical response. CONCLUSIONS These data provide further support for elevated amygdala activity in depression and suggest that anterior cingulate activity may be a predictor of treatment response to both pharmacotherapy and CBT.