This paper reviews architectonic subdivisions and connections of the orbital and medial prefrontal cortex (OMPFC) in rats, monkeys and humans. Cortico-cortical connections provide the basis for recognition of 'medial' and 'orbital' networks within the OMPFC. These networks also have distinct connections with structures in other parts of the brain. The orbital network receives sensory inputs from several modalities, including olfaction, taste, visceral afferents, somatic sensation and vision, which appear to be especially related to food or eating. In contrast, the medial network provides the major cortical output to visceromotor structures in the hypothalamus and brainstem. The two networks have distinct connections with areas of the striatum and mediodorsal thalamus. In particular, projections to the nucleus accumbens and the adjacent ventromedial caudate and putamen arise predominantly from the medial network. Both networks also have extensive connections with limbic structures. Based on these and other observations, the OMPFC appears to function as a sensory-visceromotor link, especially for eating. This linkage appears to be critical for the guidance of reward-related behavior and for setting of mood. Imaging and histological observations on human brains indicate that clinical depressive disorders are associated with specific functional and cellular changes in the OMPFC, including activity and volume changes, and specific changes in the number of glial cells.

Mood disorders are among the most common neuropsychiatric illnesses, yet little is known about their neurobiology. Recent neuroimaging studies have found that the volume of the subgenual part of Brodmann's area 24 (sg24) is reduced in familial forms of major depressive disorder (MDD) and bipolar disorder (BD). In this histological study, we used unbiased stereological techniques to examine the cellular composition of area sg24 in two different sets of brains. There was no change in the number or size of neurons in area sg24 in mood disorders. In contrast, the numbers of glia were reduced markedly in both MDD and BD. The reduction in glial number was most prominent in subgroups of subjects with familial MDD (24%, P ‫؍‬ 0.01) or BD (41%, P ‫؍‬ 0.01). The glial reduction in subjects without a clear family history was lower in magnitude and not statistically significant. Consistent with neuroimaging findings, cortical volume was reduced in area sg24 in subjects with familial mood disorders. Schizophrenic brains studied as psychiatric controls had normal neuronal and glial numbers and cortical volume. Glial and neuronal numbers also were counted in area 3b of the somatosensory cortex in the same group of brains and were normal in all psychiatric groups. Glia affect several processes, including regulation of extracellular potassium, glucose storage and metabolism, and glutamate uptake, all of which are crucial for normal neuronal activity. We thus have identified a biological marker associated with familial mood disorders that may provide important clues regarding the pathogenesis of these common psychiatric conditions.

Anticorrelated relationships in spontaneous signal fluctuation have been previously observed in resting-state functional magnetic resonance imaging (fMRI). In particular, it was proposed that there exists two systems in the brain that are intrinsically organized into anticorrelated networks, the default mode network, which usually exhibits task-related deactivations, and the task-positive network, which usually exhibits task-related activations during tasks that demands external attention. However, it is currently under debate whether the anticorrelations observed in resting state fMRI were valid or were instead artificially introduced by global signal regression, a common preprocessing technique to remove physiological and other noise in resting-state fMRI signal. We examined positive and negative correlations in resting-state connectivity using two different preprocessing methods: a component base noise reduction method (CompCor, Behzadi et al., 2007), in which principal components from noise regions-of-interest were removed, and the global signal regression method. Robust anticorrelations between a default mode network seed region in the medial prefrontal cortex and regions of the task-positive network were observed under both methods. Specificity of the anticorrelations was similar between the two methods. Specificity and sensitivity for positive correlations were higher under CompCor compared to the global regression method. Our results suggest that anticorrelations observed in resting-state connectivity are not an artifact introduced by global signal regression and might have biological origins, and that the CompCor method can be used to examine valid anticorrelations during rest.

The structure of the human orbital and medial prefrontal cortex (OMPFC) was investigated using five histological and immunohistochemical stains and was correlated with a previous analysis in macaque monkeys [Carmichael and Price (1994) J. Comp. Neurol. 346:366-402]. A cortical area was recognized if it was distinct with at least two stains and was found in similar locations in different brains. All of the areas recognized in the macaque OMPFC have counterparts in humans. Areas 11, 13, and 14 were subdivided into areas 11m, 11l, 13a, 13b, 13m, 13l, 14r, and 14c. Within area 10, the region corresponding to area 10m in monkeys was divided into 10m and 10r, and area 10o (orbital) was renamed area 10p (polar). Areas 47/12r, 47/12m, 47/12l, and 47/12s occupy the lateral orbital cortex, corresponding to monkey areas 12r, 12m, 12l, and 12o. The agranular insula (areas Iam, Iapm, Iai, and Ial) extends onto the caudal orbital surface and into the horizontal ramus of the lateral sulcus. The growth of the frontal pole in humans has pushed area 25 and area 32pl, which corresponds to the prelimbic area 32 in Brodmann's monkey brain map, caudal and ventral to the genu of the corpus callosum. Anterior cingulate areas 24a and 24b also extend ventral to the genu of the corpus callosum. Area 32ac, corresponding to the dorsal anterior cingulate area 32 in Brodmann's human brain map, is anterior and dorsal to the genu. The parallel organization of the OMPFC in monkeys and humans allows experimental data from monkeys to be applied to studies of the human cortex.

By meta-analyzing the whole-exomes of 24,248 cases and 97,322 controls, we implicate ultra-rare coding variants (URVs) in ten genes as conferring substantial risk for schizophrenia (odds ratios 3 -50, P < 2.14 x 10 -6 ), and 32 genes at a FDR < 5%. These genes have the greatest expression in central nervous system neurons and have diverse molecular functions that include the formation, structure, and function of the synapse. The associations of NMDA receptor subunit GRIN2A and AMPA receptor subunit GRIA3 provide support for the dysfunction of the glutamatergic system as a mechanistic hypothesis in the pathogenesis of schizophrenia. We find significant evidence for an overlap of rare variant risk between schizophrenia, autism spectrum disorders (ASD), and severe neurodevelopmental disorders (DD/ID), supporting a neurodevelopmental etiology for schizophrenia. We show that proteintruncating variants in GRIN2A, TRIO, and CACNA1G confer risk for schizophrenia whereas specific missense mutations in these genes confer risk for DD/ID. Nevertheless, few of the strongly associated schizophrenia genes appear to confer risk for DD/ID. We demonstrate that genes prioritized from common variant analyses of schizophrenia are enriched in rare variant risk, suggesting that common and rare genetic risk factors at least partially converge on the same underlying pathogenic biological processes. Even after excluding significantly associated genes, schizophrenia cases still carry a substantial excess of URVs, implying that more schizophrenia risk genes await discovery using this approach.

In mood disorders there is growing evidence for glutamatergic abnormalities derived from peripheral measures of glutamatergic metabolites in patients, postmortem studies on glutamate related markers, and animal studies on the mechanism of action of available treatments. Magnetic resonance spectroscopy (MRS) has the potential to corroborate and extend these findings with the advantage of in vivo assessment of glutamate-related metabolites in different disease states, in response to treatment, and in relation with functional imaging data. In this article we first review the biological significance of glutamate, glutamine, and Glx (composed mainly of glutamate and glutamine). Next we review the MRS literature in mood disorders examining these glutamate-related metabolites. Here, we find a highly consistent pattern of Glx level reductions in major depressive disorder and elevations in bipolar disorder. In addition, studies of depression regardless of diagnosis provide suggestive evidence for reduced glutamine/glutamate ratio, and in mania for elevated glutamine/ glutamate ratio. These patterns suggest that the glutamate-related metabolite pool (not all of it necessarily relevant to neurotransmission) is constricted in major depressive disorder and expanded in bipolar disorder. Depressive and manic episodes may be characterized by modulation of the glutamine/glutamate ratio in opposite directions, possibly suggesting reduced vs. elevated glutamate conversion to glutamine by glial cells, respectively. We discuss the implications of these results for the pathophysiology of mood disorders, and suggest future directions for MRS studies.

Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or “shape”) as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia.

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