Raymond Salvador

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.

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.

Functional Disconnectivity of the Medial Temporal Lobe in Asperger's Syndrome

BACKGROUND Autistic spectrum disorders (ASD) are neurodevelopmental conditions that may be caused by abnormal connectivity between brain regions constituting neurocognitive networks for specific aspects of social cognition. METHODS We used three-way multidimensional scaling of regionally parcellated functional magnetic resonance imaging (fMRI) data to explore the hypothesis of abnormal functional connectivity in people with ASD. Thirteen high-functioning individuals with Aspergers syndrome and 13 healthy volunteers were scanned during incidental processing of fearful facial expressions. RESULTS Using permutation tests for inference, we found evidence for significant abnormality of functional integration of amygdala and parahippocampal gyrus (p < .05, false discovery rate [FDR] corrected) in people with Aspergers syndrome. There were less salient abnormalities in functional connectivity of anterior cingulate, inferior occipital, and inferior frontal cortex, but there was no significant difference between groups in whole brain functional connectivity. CONCLUSIONS We conclude there is evidence that functional connectivity of medial temporal lobe structures specifically is abnormal in people with Aspergers syndrome during fearful face processing.

A simple view of the brain through a frequency-specific functional connectivity measure

Here we develop a measure of functional connectivity describing the degree of covariability between a brain region and the rest of the brain. This measure is based on previous formulas for the mutual information (MI) between clusters of regions in the frequency domain. Under the current scenario, the MI can be given as a simple monotonous function of the multiple coherence and it leads to an easy visual representation of connectivity patterns. Computationally efficient formulas, adequate for short time series, are presented and applied to functional magnetic resonance imaging (fMRI) data measured in subjects (N=34) performing a working memory task or being at rest. While resting state coherence in high (0.17-0.25 Hz) and middle (0.08-0.17 Hz) frequency intervals is bilaterally salient in several limbic and temporal areas including the insula, the amygdala, and the primary auditory cortex, low frequencies (<0.08 Hz) have greatest connectivity in frontal structures. Results from the comparison between resting and N-back conditions show enhanced low frequency coherence in many of the areas previously reported in standard fMRI activation studies of working memory, but task related reductions in high frequency connectivity are also found in regions of the default mode network. Finally, potentially confounding effects of head movement and regional volume on MI are identified and addressed.

Formal characterization and extension of the linearized diffusion tensor model.

We analyzed the properties of the logarithm of the Rician distribution leading to a full characterization of the probability law of the errors in the linearized diffusion tensor model. An almost complete lack of bias, a simple relation between the variance and the signal‐to‐noise ratio in the original complex data, and a close approximation to normality facilitated estimation of the tensor components by an iterative weighted least squares algorithm. The theory of the linear model has also been used to derive the distribution of mean diffusivity, to develop an informative statistical test for relative lack of fit of the ellipsoidal (or spherical) model compared to an unrestricted linear model in which no specific shape is assumed for the diffusion process, and to estimate the signal‐to‐noise ratios in the original data. The false discovery rate (FDR) has been used to control thresholds for statistical significance in the context of multiple comparisons at voxel level. The methods are illustrated by application to three diffusion tensor imaging (DTI) datasets of clinical interest: a healthy volunteer, a patient with acute brain injury, and a patient with hydrocephalus. Interestingly, some salient features, such as a region normally comprising the basal ganglia and internal capsule, and areas of edema in patients with brain injury and hydrocephalus, had patterns of error largely independent from their mean diffusivities. These observations were made in brain regions with sufficiently large signal‐to‐noise ratios (>2) to justify the assumptions of the log Rician probability model. The combination of diffusivity and its error may provide added value in diagnostic DTI of acute pathologic expansion of the extracellular fluid compartment in brain parenchymal tissue. Hum. Brain Mapping 24:144–155, 2005.

Wavelets and statistical analysis of functional magnetic resonance images of the human brain

Wavelets provide an orthonormal basis for multiresolution analysis and decorrelation or ‘whitening’ of nonstationary time series and spatial processes. Wavelets are particularly well suited to analysis of biological signals and images, such as human brain imaging data, which often have fractal or scale-invariant properties. We briefly define some key properties of the discrete wavelet transform (DWT) and review its applications to statistical analysis of functional magnetic resonance imaging (fMRI) data. We focus on time series resampling by ‘wavestrapping’ of wavelet coefficients, methods for efficient linear model estimation in the wavelet domain, and wavelet-based methods for multiple hypothesis testing, all of which are somewhat simplified by the decorrelating property of the DWT.

Overall brain connectivity maps show cortico-subcortical abnormalities in schizophrenia.

Abnormal interactions between areas of the brain have been pointed as possible causes for schizophrenia. However, the nature of these disturbances and the anatomical location of the regions involved are still unclear. Here, we describe a method to estimate maps of net levels of connectivity in the resting brain, and we apply it to look for differential patterns of connectivity in schizophrenia. This method uses partial coherences as a basic measure of covariability, and it minimises the effect of major physiological noise. When overall (net) connectivity maps of a sample of 40 patients with schizophrenia were compared with the maps from a matched sample of 40 controls, a single area of abnormality was found. It is an area of patient hyper‐connectivity and is located frontally, in medial and orbital structures, clearly overlapping the anterior node of the default mode network (DMN). When this area is used as a region of interest in a second‐level analysis, it shows functional hyper‐connections with several cortical and subcortical structures. Interestingly, the most significant abnormality is found with the caudate, which has a bilateral pattern of abnormality, pointing to a possible DMN–striatum deviant relation in schizophrenia. However, hyper‐connectivity observed with other regions (right hippocampus and amygdala, and other cortical structures) suggests a more pervasive alteration of brain connectivity in this disease. Hum Brain Mapp, 2010.

Direct comparison of cerebrovascular effects of norepinephrine and dopamine in head-injured patients

ObjectiveTo directly compare the cerebrovascular effects of norepinephrine and dopamine in patients with acute traumatic brain injury. DesignProspective randomized crossover trial. SettingNeurosciences critical care unit of a university hospital. PatientsTen acutely head-injured patients requiring vasoactive drugs to maintain a cerebral perfusion pressure of 65 mm Hg. InterventionsPatients were randomized to start the protocol with either norepinephrine or dopamine. Using an infusion of the allocated drug, cerebral perfusion pressure was adjusted to 65 mm Hg. After 20 mins of data collection, cerebral perfusion pressure was increased to 75 mm Hg by increasing the infusion rate of the vasoactive agent. After 20 mins of data collection, cerebral perfusion pressure was increased to 85 mm Hg and again data were collected for 20 mins. Subsequently, the infusion rate of the vasoactive drug was reduced until a cerebral perfusion pressure of 65 mm Hg was reached and the drug was exchanged against the other agent. The protocol was then repeated. Measurements and Main ResultsMean arterial pressure and intracranial pressure were monitored and cerebral blood flow was estimated with transcranial Doppler. Norepinephrine led to predictable and significant increases in flow velocity for each step increase in cerebral perfusion pressure (57.5 ± 19.9 cm·sec−1, 61.3 ± 22.3 cm·sec−1, and 68.4 ± 24.8 cm·sec−1 at 65, 75, and 85 mm Hg, respectively; p < .05 for all three comparisons), but changes with dopamine were variable and inconsistent. There were no differences between absolute values of flow velocity or intracranial pressure between the two drugs at any cerebral perfusion pressure level. ConclusionsNorepinephrine may be more predictable and efficient to augment cerebral perfusion in patients with traumatic brain injury.

A semi-automatic methodology to detect fire scars in shrubs and evergreen forests with Landsat MSS time series

This paper presents a semi-automatic methodology for fire scars mapping from a long time series of remote sensing data. Approximately, a hundred MSS images from different Landsat satellites were employed over an area of 32 100 km2 in the north-east of the Iberian Peninsula. The analysed period was from 1975 to 1993. Results are a map series of fire history and frequencies. Omission errors are 23% for burned areas greater than 200 ha while commission errors are 8% for areas greater than 50 ha. Subsequent work based on the resultant fire scars will also help in describing fire regime and in monitoring post-fire regeneration dynamics.

Age and cholinergic effects on hemodynamics and functional coherence of human hippocampus

Aging is normally associated with increased predictability of neurophysiological processes. To test the related prediction of age-related increase in the Hurst exponent, H, of functional MRI time series, and its possible cholinergic mechanisms, two groups of healthy participants (old [mean age = 65 years]; young [mean age = 22 years]; N = 11 per group) were scanned twice at rest, following placebo and a muscarinic receptor antagonist, scopolamine 0.3 mg. Older age was associated with significant increase in H of fMRI time series in bilateral hippocampus. Similarly, scopolamine was associated with increased H in left hippocampus; and there was an age-by-drug interaction in medial temporal lobe whereby older participants specifically had increased H following scopolamine. Scopolamine also enhanced fronto-hippocampal low-frequency coherence, and this could be correlated with its effect on hippocampal H. Thus, increased persistence of hippocampal dynamics in older subjects is demonstrable by resting fMRI; scopolamine mimics these effects, especially in older subjects, implying a cholinergic mechanism for age-related change; and cholinergic effects on hippocampal dynamics are associated with enhanced functional connectivity between frontal cortex and hippocampus.