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Deep brain stimulation of the subthalamic nucleus (STN DBS) has become an accepted tool for the treatment of Parkinson's disease (PD). Although the precise mechanism of action of this intervention is unknown, its effectiveness has been attributed to the modulation of pathological network activity. We examined this notion using positron emission tomography (PET) to quantify stimulation-induced changes in the expression of a PD-related covariance pattern (PDRP) of regional metabolism. These metabolic changes were also compared with those observed in a similar cohort of patients undergoing STN lesioning. We found that PDRP activity declined significantly (P < 0.02) with STN stimulation. The degree of network modulation with DBS did not differ from that measured following lesioning (P = 0.58). Statistical parametric mapping (SPM) revealed that metabolic reductions in the internal globus pallidus (GPi) and caudal midbrain were common to both STN interventions (P < 0.01), although declines in GPi were more pronounced with lesion. By contrast, elevations in posterior parietal metabolism were common to the two procedures, albeit more pronounced with stimulation. These findings indicate that suppression of abnormal network activity is a feature of both STN stimulation and lesioning. Nonetheless, these two interventions may differ metabolically at a regional level.

In this study, we tested the hypotheses that (1) the acquisition of sequential information is related to the integrity of dopaminergic input to the caudate nucleus; and (2) the integrity of dopaminergic input to the caudate nucleus correlates significantly with brain activation during sequence acquisition. Twelve early stage Parkinson's disease (PD) patients and six age-matched healthy volunteers were scanned using a dual tracer PET imaging design. All subjects were scanned with [(18)F]fluoropropyl-betaCIT (FPCIT) to measure striatal dopamine transporter (DAT) binding and with [(15)O]water to assess activation during a sequence learning task where movements were made to a repeating sequence of eight targets. Caudate and putamen DAT binding in the PD cohort was reduced by 15% and 43%, respectively. In PD, caudate DAT binding correlated with target acquisition (R = 0.57, P < 0.05), while putamen DAT binding did not correlate with performance. In volunteers, caudate DAT binding correlated with learning-related activation (P < 0.05, corrected for multiple comparisons) in the left dorsolateral and ventral prefrontal cortices, the anterior cingulate and premotor regions, and the right cerebellum. A significant correlation with caudate DAT binding was additionally detected in the right anteromedial thalamus, extending into the rostral midbrain. By contrast, in the PD cohort, most of these regional relationships were lost: Only ventral and dorsolateral prefrontal cortex activation correlated with caudate dopaminergic tone. Our findings suggest that sequence learning is normally associated with tight coupling between dopaminergic input to the caudate and thalamo-cortical functional activity. Despite minimal reductions in nigro-caudate input, PD patients demonstrate a loss of this coupling early in the disease.

We used 15O-labeled water (H215O) positron emission tomography (PET) to study eight Parkinson's disease (PD) patients with unilateral ventral intermediate (Vim) thalamic nucleus deep brain stimulation (DBS) for severe tremor. Triaxial accelerometry (TRIAX) was used during imaging to obtain on-line measures of tremor characteristics. Regional cerebral blood flow (rCBF) scans together with TRIAX recordings were collected in three stimulation conditions (OFF, MID, and ON, corresponding, respectively, to 0%, 50%, and 100% reductions in mean accelerometry signal). Statistical Parametric Mapping (SPM99) revealed significant rCBF reductions during stimulation in the ipsilateral sensorimotor cortex (SMC) and the contralateral cerebellum, as well as concurrent increases in the ipsilateral ventral thalamus (P < 0.05, corrected). Covariate analysis of rCBF with physiological tremor characteristics revealed that tremor acceleration correlated positively with changes in the SMC and supplementary motor cortex ipsilaterally (P < 0.05, uncorrected), and negatively with changes in the ipsilateral cuneus (P < 0.05, corrected). After removing tremor acceleration effects, changes in tremor frequency correlated negatively with changes in the contralateral dentate nucleus and pons (P < 0.05, uncorrected). Our results suggest that Vim DBS for PD tremor modulates the activity of cerebello-thalamo-cortical pathways. Specific tremor characteristics relate to activity in different nodes of this system.

Simulated abnormalities were introduced in a normal SPECT with known and controllable characteristics (abnormality size and depth) in an attempt to provide validation for the analysis of SPECT lesion studies using SPM. Two simulations were carried out. The first determined the minimum hypoperfusion depth detectable using SPM by altering mean local intensity while keeping the size of the lesion constant. This was done by changing the mean local intensity in percentile increments of 10 down to −100 and up to 50. The second simulation determined the cluster size that SPM can detect by keeping the mean intensity of the lesion constant while altering its size from 4 voxels to 63,000 voxels in a total brain volume of 300,000 voxels. Both simulations determined which method of normalization is most appropriate, what level of grey matter thresholding should be used, and at what statistical probability peak threshold (u) the results should be determined. Proportional scaling was found to be the most appropriate normalization method. ANCOVA was useful where very large abnormalities were present and normalization external to SPM was not available. In those cases, ANCOVA was used in conjunction with measurement of an unaffected part of the brain (in this case medial occipital lobe). For better results statistical probability peak threshold was set to p(u) = 0.01 and grey matter threshold was set to a value below 0.5. SPM produced best results when the abnormality represented a decrease of about −50% from the normal or more and detected other decreases in an acceptable manner.

The human neuregulin-1 (NRG-1) gene is highly expressed in the brain, is implicated in numerous functions associated with neuronal development, and is a leading candidate gene for schizophrenia. The T allele of SNP8NRG243177, part of a risk haplotype for schizophrenia, has been previously associated with decreases in white matter in the right anterior internal capsule and the left anterior thalamic radiation. To our knowledge no studies have described the effects of SNP8NRG243177 on grey matter volume at a voxelwise level. We assessed associations between this SNP and brain structure in 79 general population volunteers from the Northern Finland 1966 Birth Cohort (NFBC 1966). We show, for the first time, that genetic variation in SNP8NRG243177 is associated with variation in frontal brain structure in both grey and white matter. T allele carriers showed decreased grey matter volume in several frontal gyri, including inferior, middle and superior frontal gyri and the anterior cingulate gyrus, as well as decreased white matter volume in the regions of the genu and body of the corpus callosum, anterior and superior corona radiata, anterior limb of the internal capsule and external capsule regions traversed by major white matter tracts of the anterior thalamic radiation, and the inferior fronto-occipital fasciculus. These results suggest that this genetic variant may mediate risk for schizophrenia, in part, through its effect on brain structure in these regions.

A key challenge for systems neuroscience is the question of how to understand the complex network organization of the brain on the basis of neuroimaging data. Similar challenges exist in other specialist areas of systems biology because complex networks emerging from the interactions between multiple non-trivially interacting agents are found quite ubiquitously in nature, from protein interactomes to ecosystems. We suggest that one way forward for analysis of brain networks will be to quantify aspects of their organization which are likely to be generic properties of a broader class of biological systems. In this introductory review article we will highlight four important aspects of complex systems in general: fractality or scale-invariance; criticality; small-world and related topological attributes; and modularity. For each concept we will provide an accessible introduction, an illustrative data-based example of how it can be used to investigate aspects of brain organization in neuroimaging experiments, and a brief review of how this concept has been applied and developed in other fields of biomedical and physical science. The aim is to provide a didactic, focussed and user-friendly introduction to the concepts of complexity science for neuroscientists and neuroimagers.

The differential diagnosis of parkinsonian disorders can be challenging, especially early in the disease course. PET imaging with [(18)F]-fluorodeoxyglucose (FDG) has been used to identify characteristic patterns of regional glucose metabolism in patient cohorts with idiopathic Parkinson's disease (PD), as well as variant forms of parkinsonism such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBGD). In this study, we assessed the utility of FDG PET in the differential diagnosis of individual patients with clinical parkinsonism. 135 parkinsonian patients were referred for FDG PET to determine whether their diagnosis could be made accurately based upon their scans. Imaging-based diagnosis was obtained by visual assessment of the individual scans and also by computer-assisted interpretation. The results were compared with 2-year follow-up clinical assessments made by independent movement disorders specialists who were blinded to the original PET findings. We found that blinded computer assessment agreed with clinical diagnosis in 92.4% of all subjects (97.7% early PD, 91.6% late PD, 96% MSA, 85% PSP, 90.1% CBGD, 86.5% healthy control subjects). Concordance of visual inspection with clinical diagnosis was achieved in 85.4% of the patients scanned (88.4% early PD, 97.2% late PD, 76% MSA, 60% PSP, 90.9% CBGD, 90.9% healthy control subjects). This study demonstrates that FDG PET performed at the time of initial referral for parkinsonism accurately predicted the clinical diagnosis of individual patients made at subsequent follow-up. Computer-assisted methodologies may be particularly helpful in situations where experienced readers of FDG PET images are not readily available.