Dorsal Anterior Cingulate Cortices Differentially Lateralize Prediction Errors and Outcome Valence in a Decision-Making Task.

The dorsal anterior cingulate cortex (dACC) is proposed to facilitate learning by signaling mismatches between the expected outcome of decisions and the actual outcomes in the form of prediction errors. The dACC is also proposed to discriminate outcome valence—whether a result has positive (either expected or desirable) or negative (either unexpected or undesirable) value. However, direct electrophysiological recordings from human dACC to validate these separate, but integrated, dimensions have not been previously performed. We hypothesized that local field potentials (LFPs) would reveal changes in the dACC related to prediction error and valence and used the unique opportunity offered by deep brain stimulation (DBS) surgery in the dACC of three human subjects to test this hypothesis. We used a cognitive task that involved the presentation of object pairs, a motor response, and audiovisual feedback to guide future object selection choices. The dACC displayed distinctly lateralized theta frequency (3–8 Hz) event-related potential responses—the left hemisphere dACC signaled outcome valence and prediction errors while the right hemisphere dACC was involved in prediction formation. Multivariate analyses provided evidence that the human dACC response to decision outcomes reflects two spatiotemporally distinct early and late systems that are consistent with both our lateralized electrophysiological results and the involvement of the theta frequency oscillatory activity in dACC cognitive processing. Further findings suggested that dACC does not respond to other phases of action-outcome-feedback tasks such as the motor response which supports the notion that dACC primarily signals information that is crucial for behavioral monitoring and not for motor control

Novel fingerprinting method characterises the necessary and sufficient structural connectivity from deep brain stimulation electrodes for a successful outcome

Deep brain stimulation (DBS) is a remarkably effective clinical tool, used primarily for movement/ndisorders. DBS relies on precise targeting of specific brain regions to rebalance the oscillatory behaviour/nof whole-brain neural networks. Traditionally, DBS targeting has been based upon animal/nmodels (such asMPTPfor Parkinson’s disease) but has also been the result of serendipity during/nhuman lesional neurosurgery. There are, however, no good animal models of psychiatric disorders/nsuch as depression and schizophrenia, and progress in this area has been slow. In this paper, we use/nadvanced tractography combined with whole-brain anatomical parcellation to provide a rational/nfoundation for identifying the connectivity ‘fingerprint’ of existing, successful DBS targets. This/nknowledge can then be used pre-surgically and even potentially for the discovery of novel targets. First,/nusing data from our recent case series of cingulate DBS for patients with treatment-resistant chronic/npain, we demonstrate how to identify the structural ‘fingerprints’ of existing successful and unsuccessful/nDBS targets in terms of their connectivity to other brain regions, as defined by the whole-brain/nanatomical parcellation. Second, we use a number of different strategies to identify the successful fingerprints/nof structural connectivity across four patients with successful outcomes compared with/ntwo patients with unsuccessful outcomes. This fingerprinting method can potentially be used presurgically/nto account for a patient’s individual connectivity and identify the best DBS target. Ultimately,/nour novel fingerprinting method could be combined with advanced whole-brain computational/nmodelling of the spontaneous dynamics arising from the structural changes in disease, to/nprovide new insights and potentially new targets for hitherto impenetrable neuropsychiatric/ndisorders.We thank Ms Eloise Starkfor her valuable comments. MLK was supported by the ERC ConsolidatorGrant:/nCAREGIVING (n. 615539) and the TrygFonden Charitable Foundation. GD was supported by the ERC Advanced/nGrant: DYSTRUCTURE (n. 295129), by the Spanish Research Project SAF2010-16085 and the FP7-ICT BrainScales

Spatial and temporal distribution of information processing in the human dorsal anterior cingulate cortex

The dorsal anterior cingulate cortex (dACC) is a key node in the human salience network. It has been ascribed motor, pain-processing and affective functions. However, the dynamics of information flow in this complex region and how it responds to inputs remain unclear and are difficult to study using non-invasive electrophysiology. The area is targeted by neurosurgery to treat neuropathic pain. During deep brain stimulation surgery, we recorded local field potentials from this region in humans during a decision-making task requiring motor output. We investigated the spatial and temporal distribution of information flow within the dACC. We demonstrate the existence of a distributed network within the anterior cingulate cortex where discrete nodes demonstrate directed communication following inputs. We show that this network anticipates and responds to the valence of feedback to actions. We further show that these network dynamics adapt following learning. Our results provide evidence for the integration of learning and the response to feedback in a key cognitive region