Motion artifacts are a significant source of noise in many functional near-infrared spectroscopy (fNIRS) experiments. Despite this, there is no well-established method for their removal. Instead, functional trials of fNIRS data containing a motion artifact are often rejected completely. However, in most experimental circumstances the number of trials is limited, and multiple motion artifacts are common, particularly in challenging populations. Many methods have been proposed recently to correct for motion artifacts, including principle component analysis, spline interpolation, Kalman filtering, wavelet filtering and correlation-based signal improvement. The performance of different techniques has been often compared in simulations, but only rarely has it been assessed on real functional data. Here, we compare the performance of these motion correction techniques on real functional data acquired during a cognitive task, which required the participant to speak aloud, leading to a low-frequency, low-amplitude motion artifact that is correlated with the hemodynamic response. To compare the efficacy of these methods, objective metrics related to the physiology of the hemodynamic response have been derived. Our results show that it is always better to correct for motion artifacts than reject trials, and that wavelet filtering is the most effective approach to correcting this type of artifact, reducing the area under the curve where the artifact is present in 93% of the cases. Our results therefore support previous studies that have shown wavelet filtering to be the most promising and powerful technique for the correction of motion artifacts in fNIRS data. The analyses performed here can serve as a guide for others to objectively test the impact of different motion correction algorithms and therefore select the most appropriate for the analysis of their own fNIRS experiment.

Cognitive neuroscience is a multidisciplinary field focused on the exploration of the neural substrates underlying cognitive functions; it originated in the early 1980s from the connection between neuroscience and cognitive science although over the years it has constantly been enriched by an increasing interaction with several other disciplines, 1 such as neuro physiology, neuroanatomy, neuropsychology, psychophysiology and computational modelling. Nowadays, cognitive neuro science represents a prominent field in the investigation of the human brain. Due to its multidisciplinary nature, cognitive neuroscience adopts several investigation methods, such as lesion studies, multiunit and singlecell recording; never theless, the most remarkable progress in understanding the relationship between brain and cognition has been made with functional brain imaging methods.Before the advent of brain imaging, the association between brain regions and cognitive functions was mainly provided by clinical neuropsychological investigations of braindamaged patients and postmortem examination. When brain imaging was introduced, cognitive scientists were given the chance to investigate the human brain in a wide variety of actions, from perception to higher order mental activities. With brain Cognitive neuroscience is a multidisciplinary field focused on the exploration of the neural substrates underlying cognitive functions; the most remarkable progress in understanding the relationship between brain and cognition has been made with functional brain imaging. Functional near infrared (fNIR) spectroscopy is a non-invasive brain imaging technique that measures the variation of oxygenated and deoxygenated haemoglobin at high temporal resolution. Stemming from the first pioneering experiments, the use of fNIR spectroscopy in cognitive neuroscience has constantly increased. Here, we present a brief review of the fNIR spectroscopy investigations in the cognitive neuroscience field. The topics discussed encompass the classical issues in cognitive neuroscience, such as the exploration of the neural correlates of vision, language, memory, attention and executive functions. Other relevant research topics are introduced in order to show the strengths and the limitations of fNIR spectroscopy, as well as its potential in the biomedical field. This review is intended to provide a general view of the wide variety of optical imaging applications in the field of cognitive neuroscience. The increasing body of studies and the constant technical improvement suggest that fNIR spectroscopy is a versatile and promising instrument to investigate the neural correlates of human cognition.

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Walking is a complex motor behavior with a special relevance in clinical neurology. Many neurological diseases, such as Parkinson's disease and stroke, are characterized by gait disorders whose neurofunctional correlates are poorly investigated. Indeed, the analysis of real walking with the standard neuroimaging techniques poses strong challenges, and only a few studies on motor imagery or walking observation have been performed so far. Functional near-infrared spectroscopy (fNIRS) is becoming an important research tool to assess functional activity in neurological populations or for special tasks, such as walking, because it allows investigating brain hemodynamic activity in an ecological setting, without strong immobility constraints. A systematic review following PRISMA guidelines was conducted on the fNIRS-based examination of gait disorders. Twelve of the initial yield of 489 articles have been included in this review. The lesson learnt from these studies suggest that oxy-hemoglobin levels within the prefrontal and premotor cortices are more sensitive to compensation strategies reflecting postural control and restoration of gait disorders. Although this field of study is in its relative infancy, the evidence provided encourages the translation of fNIRS in clinical practice, as it offers a unique opportunity to explore in depth the activity of the cortical motor system during real walking in neurological patients. We also discuss to what extent fNIRS may be applied for assessing the effectiveness of rehabilitation programs.

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