In the absence of external stimuli, human hemodynamic brain activity displays slow intrinsic variations. To find out whether such fluctuations would be altered by persistent pain, we asked 10 patients with unrelenting chronic pain of different etiologies and 10 sex-and agematched control subjects to rest with eyes open during 3-T functional MRI. Independent component analysis was used to identify functionally coupled brain networks. Time courses of an independent component comprising the insular cortices of both hemispheres showed stronger spectral power at 0.12 to 0.25 Hz in patients than in control subjects, with the largest difference at 0.16 Hz. A similar but weaker effect was seen in the anterior cingulate cortex, whereas activity of the precuneus and early visual cortex, used as a control site, did not differ between the groups. In the patient group, seed pointbased correlation analysis revealed altered spatial connectivity between insulae and anterior cingulate cortex. The results imply both temporally and spatially aberrant activity of the affective painprocessing areas in patients suffering from chronic pain. The accentuated 0.12-to 0.25-Hz fluctuations in the patient group might be related to altered activity of the autonomic nervous system. functional MRI | insula | resting state | autonomic nervous system | human A cute pain has an important protective function and is supported by a well-known brain network comprising the insular cortex, anterior cingulate cortex (ACC), primary and secondary somatosensory cortex, and thalamus (1). When pain becomes chronic, its physiological protective function is lost. Chronic pain decreases the quality of life and interferes with the cognitive, affective, and physical functioning. Although one-fifth of the Western population suffers from chronic pain (2), the underlying brain activity is poorly understood.Extensive meta-analyses (1, 3, 4) indicate that the brain areas related to chronic and acute pain differ to some extent, but no single brain-activity pattern is specific to chronic pain. Morphometric analyses suggest gray-matter loss in many chronic pain conditions, indicating that chronic pain may alter brain structure (5), but in a reversible manner (6).Previous studies on the brain basis of chronic pain have concentrated on abnormal activation sites and strengths following external stimulation. Studies of resting-state brain activity by means of functional magnetic resonance imaging (fMRI) have shown that the connectivity within the default-mode network (7) is altered in chronic pain, together with reduced task-related deactivation within this network (8, 9). Recently, the spectra of the default-mode network were shown to contain more power at 0.05 to 0.1 Hz in patients suffering from diabetic neuropathic pain than in healthy control subjects (9).In the present study, we focused on the resting-state fluctuations and functional connectivity of the affective pain-processing areas, the insula and ACC, in chronic pain. Specifically, we recorded spontaneous fMRI sig...

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Rewards associated with actions are critical for motivation and learning about the consequences of one’s actions on the world. The motor cortices are involved in planning and executing movements, but it is unclear whether they encode reward over and above limb kinematics and dynamics. Here, we report a categorical reward signal in dorsal premotor (PMd) and primary motor (M1) neurons that corresponds to an increase in firing rates when a trial was not rewarded regardless of whether or not a reward was expected. We show that this signal is unrelated to error magnitude, reward prediction error, or other task confounds such as reward consumption, return reach plan, or kinematic differences across rewarded and unrewarded trials. The availability of reward information in motor cortex is crucial for theories of reward-based learning and motivational influences on actions.

Current knowledge about the precise timing of visual input to the cortex relies largely on spike timings in monkeys and evoked-response latencies in humans. However, quantifying the activation onset does not unambiguously describe the timing of stimulus-feature-specific information processing. Here, we investigated the information content of the early human visual cortical activity by decoding low-level visual features from single-trial magnetoencephalographic (MEG) responses. MEG was measured from nine healthy subjects as they viewed annular sinusoidal gratings (spanning the visual field from 2 to 10°for a duration of 1 s), characterized by spatial frequency (0.33 cycles/degree or 1.33 cycles/degree) and orientation (45°or 135°); gratings were either static or rotated clockwise or anticlockwise from 0 to 180°. Time-resolved classifiers using a 20 ms moving window exceeded chance level at 51 ms (the later edge of the window) for spatial frequency, 65 ms for orientation, and 98 ms for rotation direction. Decoding accuracies of spatial frequency and orientation peaked at 70 and 90 ms, respectively, coinciding with the peaks of the onset evoked responses. Within-subject time-insensitive pattern classifiers decoded spatial frequency and orientation simultaneously (mean accuracy 64%, chance 25%) and rotation direction (mean 82%, chance 50%). Classifiers trained on data from other subjects decoded the spatial frequency (73%), but not the orientation, nor the rotation direction. Our results indicate that unaveraged brain responses contain decodable information about low-level visual features already at the time of the earliest cortical evoked responses, and that representations of spatial frequency are highly robust across individuals.

How to move efficiently is an optimal control problem, whose computational complexity grows exponentially with the horizon of the planned trajectory. Breaking a compound movement into a series of chunks, each planned over a shorter horizon can thus reduce the overall computational complexity and associated costs while limiting the achievable efficiency. This trade-off suggests a cost-effective learning strategy: to learn new movements we should start with many short chunks (to limit the cost of computation). As practice reduces the impediments to more complex computation, the chunking structure should evolve to allow progressively more efficient movements (to maximize efficiency). Here we show that monkeys learning a reaching sequence over an extended period of time adopt this strategy by performing movements that can be described as locally optimal trajectories. Chunking can thus be understood as a cost-effective strategy for producing and learning efficient movements.