Visual face identification requires distinguishing between thousands of faces we know. This computational feat involves a network of brain regions including the fusiform face area (FFA) and anterior inferotemporal cortex (aIT), whose roles in the process are not well understood. Here, we provide the first demonstration that it is possible to discriminate cortical response patterns elicited by individual face images with high-resolution functional magnetic resonance imaging (fMRI). Response patterns elicited by the face images were distinct in aIT but not in the FFA. Individual-level face information is likely to be present in both regions, but our data suggest that it is more pronounced in aIT. One interpretation is that the FFA detects faces and engages aIT for identification.fMRI ͉ information-based ͉ population code W hen we perceive a familiar face, we usually effortlessly recognize its identity. Identification requires distinguishing between thousands of faces we know. A puzzle to both brain and computer scientists, this computational feat involves a network of brain regions (1) including the fusiform face area (FFA) (2, 3) and anterior inferotemporal cortex (aIT) (4). There is a wealth of evidence for an involvement in face identification of both the FFA (1, 5-18) and aIT (4,16,(19)(20)(21)(22)(23)(24)(25)(26).The FFA responds vigorously whenever a face is perceived (2,3,27). This implies that the FFA distinguishes faces from objects of other categories and suggests the function of face detection (27,28). An additional role for the FFA in face identification has been suggested by three lines of evidence: (i) Lesions in the region of the FFA are frequently associated with deficits at recognizing individual faces (prosopagnosia) (6, 9, 10). (ii) The FFA response level covaries with behavioral performance at identification (11). (iii) The FFA responds more strongly to a sequence of different individuals than to the same face presented repeatedly (8,(12)(13)(14)(15)(16)(17).For aIT as well, human lesion and neuroimaging studies suggest a role in face identification. Neuroimaging studies (4,(22)(23)(24)26) found anterior temporal activation during face recognition with the activity predictive of performance (22). Lesion studies (19,20,25) suggest that right anterior temporal cortex is involved in face identification. In monkey electrophysiology, in fact, face-identity effects appear stronger in anterior than in posterior inferotemporal cortex (29-31).These lines of evidence suggest an involvement of both the FFA and aIT in face identification. A region representing faces at the individual level should distinguish individual faces by its activity pattern. However, it has never been directly demonstrated that either the FFA or aIT responds with distinct activity patterns to different individual faces.We therefore investigated response patterns elicited by two face images by means of high-resolution functional magnetic resonance imaging (fMRI) at 3 Tesla (voxels: 2 ϫ 2 ϫ 2 mm 3 ). We asked whether response pattern...

Many patients show no or incomplete responses to current pharmacological or psychological therapies for depression. Here we explored the feasibility of a new brain self-regulation technique that integrates psychological and neurobiological approaches through neurofeedback with functional magnetic resonance imaging (fMRI). In a proof-of-concept study, eight patients with depression learned to upregulate brain areas involved in the generation of positive emotions (such as the ventrolateral prefrontal cortex (VLPFC) and insula) during four neurofeedback sessions. Their clinical symptoms, as assessed with the 17-item Hamilton Rating Scale for Depression (HDRS), improved significantly. A control group that underwent a training procedure with the same cognitive strategies but without neurofeedback did not improve clinically. Randomised blinded clinical trials are now needed to exclude possible placebo effects and to determine whether fMRI-based neurofeedback might become a useful adjunct to current therapies for depression.

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

Speech is crucial for communication in everyday life. Speech-brain entrainment, the alignment of neural activity to the slow temporal fluctuations (envelope) of acoustic speech input, is a ubiquitous element of current theories of speech processing. Associations between speech-brain entrainment and acoustic speech signal, listening task, and speech intelligibility have been observed repeatedly. However, a methodological bottleneck has prevented so far clarifying whether speech-brain entrainment contributes functionally to (i.e., causes) speech intelligibility or is merely an epiphenomenon of it. To address this long-standing issue, we experimentally manipulated speech-brain entrainment without concomitant acoustic and task-related variations, using a brain stimulation approach that enables modulating listeners' neural activity with transcranial currents carrying speech-envelope information. Results from two experiments involving a cocktail-party-like scenario and a listening situation devoid of aural speech-amplitude envelope input reveal consistent effects on listeners' speech-recognition performance, demonstrating a causal role of speech-brain entrainment in speech intelligibility. Our findings imply that speech-brain entrainment is critical for auditory speech comprehension and suggest that transcranial stimulation with speech-envelope-shaped currents can be utilized to modulate speech comprehension in impaired listening conditions.

fMRI Neurofeedback research employs many different control conditions. Currently, there is no consensus as to which control condition is best, and the answer depends on what aspects of the neurofeedback-training design one is trying to control for. These aspects can range from determining whether participants can learn to control brain activity via neurofeedback to determining whether there are clinically significant effects of the neurofeedback intervention. Lack of consensus over criteria for control conditions has hampered the design and interpretation of studies employing neurofeedback protocols. This paper presents an overview of the most commonly employed control conditions currently used in neurofeedback studies and discusses their advantages and disadvantages. Control conditions covered include no control, treatment-as-usual, bidirectional-regulation control, feedback of an alternative brain signal, sham feedback, and mental-rehearsal control. We conclude that the selection of the control condition(s) should be determined by the specific research goal of the study and best procedures that effectively control for relevant confounding factors.