# A tight relationship between BOLD fMRI activation/deactivation and increase/decrease in single neuron responses in human association cortex

Marie-Alphée Laurent, Coretin Jacques, Xiaoqian Yan, Pauline Jurczynski, Sophie Colnat-Coulbois, Louis Maillard, Steven Le Cam, Radu Ranta, Benoit Cottereau, Laurent Koessler, et al.

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## To cite this version:

Marie-Alphée Laurent, Coretin Jacques, Xiaoqian Yan, Pauline Jurczynski, Sophie Colnat-Coulbois, et al. A tight relationship between BOLD fMRI activation/deactivation and increase/decrease in single neuron responses in human association cortex. eLife, 2025, 14, pp.RP104779. 10.7554/eLife.104779. hal-04793156v1

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Submitted on 20 Nov 2024 (v1), last revised 27 Nov 2025 (v2)

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# A tight relationship between BOLD fMRI activation/deactivation and increase/decrease in single neuron responses in human association cortex

Marie-Alphée Laurentª, Corentin Jacquesª, Xiaoqian Yanᵇ, Pauline Jurczynskiᶜ,
Sophie Colnat-Coulboisª,ᵈ, Louis Maillardª,ᵉ, Steven Le Camᶜ, Radu Rantaᶜ, Benoit
R. Cottereauf, Laurent Koesslerª, Jacques Jonasª,ᵉ, Bruno Rossionª,ᵉ

ªIMoPA, CNRS, Université de Lorraine, F-54000 Nancy, France

ᵇFudan University, Institute of Science and Technology for Brain-Inspired
Intelligence, 200433, Shanghai, China

ᶜUniversité de Lorraine, CNRS, CRAN, F-54000 Nancy, France

ᵈUniversité de Lorraine, CHRU-Nancy, Service de Neurochirurgie, F-54000 Nancy,
France

ᵉUniversité de Lorraine, CHRU-Nancy, Service de Neurologie, F-54000 Nancy,
France

fCentre de Recherche Cerveau et Cognition, Université Toulouse 3 Paul Sabatier,
CNRS, 31052 Toulouse, France

**Corresponding authors:**

Marie-Alphée Laurent and Bruno Rossion

marie-alphee.laurent@univ-lorraine.fr

bruno.rossion@univ-lorraine.fr

Hôpital Central, Pavillon Krug
CHRU-Nancy
29 Avenue du Maréchal de Lattre de Tassigny
54000 NANCY

**Competing Interest Statement:** The authors declare no competing interest.

**Keywords:** single neurons; fMRI; face-selectivity; intracerebral recordings; BOLD activation; BOLD deactivation
1 Abstract

2 The relationship between Blood-Oxygen-Level-Dependent (BOLD) responses in
3 functional magnetic resonance imaging (fMRI) and increases or decreases in neural
4 firing rate across human brain regions, especially the association cortex, remains
5 largely unknown. Here, we contrast direct measures of neuronal activity in two
6 adjacent brain regions of the fusiform gyrus (FG) associated with fMRI increases
7 (lateral FG portion) or decreases (medial FG portion) of the same category-selective
8 neural activity. In both individual brains tested across multiple recording sessions, a
9 frequency-tagging stimulation objectively identified a substantial proportion (about
10 70%) of face-selective neurons. While single-units recorded in the lateral FG showed
11 a selective increase to faces, neurons localized in the medial FG decreased spiking
12 activity selectively to faces. Beyond a relative reduction to faces compared to non-
13 face objects, about a third of single neurons found in the medial FG showed genuine
14 suppression of baseline spiking activity upon presentation of a face. These
15 observations clarify the nature of face-selective neural activity in the human brain,
16 which can be expressed both as increases and active suppressions of spiking
17 activity, and, more generally, shed light on the physiological basis of the fMRI signal.
18 **Introduction**

19 Functional magnetic resonance imaging (fMRI) has revolutionized our 20 understanding of human brain function. While animal studies have characterized 21 Blood-Oxygenation-Level-Dependent (BOLD) fMRI activation as a correlate of local 22 synaptic activity (Logothetis et al., 2001; Bartolo et al., 2011), the cellular 23 mechanisms of deactivation (negative BOLD signals) remain unknown. Since BOLD 24 activity relies on contrasts between (two) conditions of interest, fMRI deactivations 25 have been attributed to various causes, i.e., (relative) decreases in neuronal activity 26 (Shmuel et al., 2006; Devor et al., 2008; Boorman et al., 2010), (relative) increases 27 in neural activity (without concomitant compensation by cerebral blood flow (CBF) 28 increase) (Schridde et al., 2008), or hemodynamic contributions ("blood stealing"; 29 Harel et al., 2002). However, notwithstanding recent evidence of fMRI deactivation 30 associated with electrocorticographic alpha power decrease in the human visual 31 cortex (Fracasso et al., 2021), most studies have been carried out in animal models 32 and primary sensory or motor regions. Therefore, the relationship between BOLD 33 responses and increases or decreases in neuronal firing rate across human brain 34 regions, especially the association cortex, remains unknown.

35 Here, thanks to a unique opportunity to record fMRI and spiking activity in 36 neighboring populations of neurons of the same region of the association cortex, we 37 shed light on the nature of the relationship between category-selective activity 38 recorded at macroscopic and cellular scales. Specifically, two patients with refractory 39 epilepsy (P1, P2) were implanted with hybrid macro-microelectrodes in their fusiform 40 gyrus, a hominoid-specific cortical structure (Weiner and Zilles, 2016) that is critically 41 involved in face recognition (Cohen et al., 2019). While microelectrodes recording 42 single-unit activity located in the lateral portion of P1's middle fusiform gyrus (MidFG) 43 (i.e., in cytoarchitectonic area FG4; Lorenz et al., 2017), a region known to elicit 44 larger BOLD activity to faces than non-face objects ('Fusiform Face Area', FFA; 45 Kanwisher et al., 1997), in P2 they fell in the medial MidFG portion (FG3), in which 46 lower BOLD responses to faces than objects are typically observed (Pelphrey et al., 47 2003; Kanwisher, 2017; Gao et al., 2018). By recording face-selective neural 48 responses both in spiking activity and fMRI signals (independent sessions) in two 49 neighboring regions of the same brains, we tested the hypothesis of a systematic 50 relationship between the two types of signals. In particular, we hypothesized that
51 fMRI deactivations to faces were associated with a majority of face-selective decreases in spiking activity.
52

## 53 Materials and Methods

54 **Participants.** The study included two right-handed participants (aged 23 and 46) who were patients undergoing clinical intracerebral evaluation with depth electrodes (SEEG; Talairach and Bancaud, 1973) for refractory partial epilepsy in the 55 Epilepsy Unit of the University Hospital of Nancy, France in 2019 (P1) and 2023 56 (P2). The participants gave written informed consent to participate to the study 57 (REUNIE, 2015-A01951-48), which was approved by a national ethical committee 58 (CPP Est III, N°16.02.01). 59
60

61 **Intracerebral recordings acquisition.** Electrophysiological data were 62 recorded from Behnke-Fried hybrid micro-macro electrodes (Ad-Tech Medical 63 Instrument). Intracerebral depth electrodes were stereotactically implanted within the 64 participant's brains to delineate their seizure onset zone for clinical purposes. 65 Electrode implantation sites were determined based on non-invasive data collected 66 during the early stages of the investigation and were verified by CT-scan fused with 67 a pre-operative T1-weighted MRI. The present report focuses on recordings from the 68 macro-microelectrodes, i.e., electrodes with macro-contacts modified to include 8 69 recording microwires and one reference, protruding about 3 to 4 mm beyond the tip 70 of the macro-electrode (Ad-Tech Medical; see Salado et al., 2018 for details about 71 the macroelectrode implantation procedure). The macro-microelectrodes targeted 72 the right (P1) and the left (P2) MidFG. Although differential recordings in the same 73 hemisphere(s) and individual brains would have been optimal, our results are 74 unlikely to be due to differences between the two individuals or hemispheres 75 reported, for two reasons. First, notwithstanding the right hemisphere's dominance in 76 human face recognition (Jonas et al., 2016; Kanwisher, 2017; Gao et al., 2018), the 77 same contrasted pattern of BOLD activation/deactivation for faces in the 78 lateral/medial portions of the fusiform gyrus, respectively, has been systematically 79 observed across hemispheres (Pelphrey et al., 2003; **Fig. 2**). Second, while a larger 80 number of single-units were found for P1 than P2 recordings, the proportions of face- 81 selective units did not differ between the two individuals.
The microwires signal was recorded at a 30 kHz sampling rate, using a 256-channel amplifier (BlackRock Microsystems), and recording activity was band-pass filtered (0.3-7500 Hz). Here we report microelectrode data recorded during 3 days (8 sequences performed in P1) and 11 days (14 sequences performed in P2). These differences across participants were due to the clinical context in which the experiment took place. Participants were tested individually and seated at 80 cm from the computer screen.

**Microwires localization in the individual anatomy.** The exact position of each contact from the micro-macro electrode relative to the individual brain anatomy was determined in each participant's brain by coregistrating the post-operative CT-scan with a T1-weighted MRI of the patient. The microwires coordinates were estimated at approximately 4 mm from the tip of the macroelectrode. In addition, the location of microwires relative to fMRI-defined face-selective regions was determined by having all participants perform an fMRI face localizer a few months after the SEEG procedure, using an fMRI version of the frequency-tagging paradigm providing high SNR and reliability (Gao et al., 2018). After fMRI scanning, estimated microelectrode coordinates were rendered into the T1-weighted MRI co-registered and normalized to ACPC space with the functional Z-score map, using *MRICROGL* for visualization. The location of the microelectrodes was also defined in relation to the boundaries of the cytoarchitectonic areas FG2/FG4 and FG1/FG3 (Rosenke et al., 2017).

**Stimuli.** Stimuli were selected from a large pool of 200 to 218 non-face object images and 45 to 48 face images (as in e.g., Jonas et al., 2016), presented in grayscale or color sequences. Each image contained an unsegmented object or face near the center, varying significantly in size, viewpoint, lighting conditions, and background (Fig. 1). Images were equalized for mean pixel luminance and contrast, but low-level visual cues associated with the two categories (faces and objects) remained highly variable, naturally minimizing the contribution of low-level visual cues to the face-selective neural responses (Rossion et al., 2015).

**Face localizer paradigms.** A well-validated Fast Periodic Visual Stimulation (FPVS) face localizer paradigm was performed to define face-selective neural activity. Highly variable natural images of faces were embedded periodically within a
rapid 6 Hz stream of object images. In the fMRI version (from Gao et al., 2018; also, Laurent et al., 2023), mini-face blocks ("bursts") were presented for 2.167 s every 9 s to account for the sluggishness of the BOLD response (i.e., F = 1/9 = 0.111 Hz). Each mini-block consisted of a set of seven face images alternated with six non-face object images to avoid category adaptation and maximize the contrast between faces and objects (Fig. 1A). Each fMRI sequence lasted 405 s and contained 44 cycles of face bursts (including a 4.5 s baseline at both the beginning and end of each sequence, respectively). During intracerebral recordings, highly variable natural images of faces were inserted periodically every fifth image (i.e., face-selective frequency at 1.2 Hz = 6/5 Hz) as usually performed (Fig. 1B; Rossion et al., 2015; Jonas et al., 2016). A stimulation sequence lasted 70 s, including 66 s of stimulation at full contrast flanked by 2 s of fade-in and fade-out, with gradual increases and decreases in contrast, respectively. All images were presented with a sinusoidal stimulation contrast to provide a smooth transition between successive images. Participants were unaware of the periodicity of the faces. During both fMRI and intracerebral recordings, participants stared at a small black cross presented at the center of the stimuli and detected rare brief nonperiodic color changes (70-10 times per sequence for fMRI/iEEG respectively, for 500 ms) of the fixation cross (black to red). The experiments were conducted using Matlab for P1 and Java 8 for P2.

**Spike sorting.** Spike detection and clustering were carried out using an automatic algorithm, based on a Bayesian approach (Le Cam et al., 2023). The clusters were visually reviewed and classified into single-units (SU) and multi-units (MU) based on their spike shape, variance, inter-spike interval distribution, and the presence of a refractory period. Of all the sessions held by the two participants, 299 SU and 62 MU were identified. Across all independent sessions (i.e., recorded on different half-days), 206 SU and 39 MU were retained for analyses. Signal processing and frequency-domain analyses were similar to previous SEEG studies (Jonas et al., 2016; Hagen et al., 2020). An average of 2 units per contact were isolated.

**Intracerebral recordings analyses.** Analyses were performed using the free software *Letswave 6*, running on MATLAB R2022a. A Fast Fourier Transform (FFT) was applied to the iEEG recordings, and the amplitude spectrum was cut into 1 Hz
segments centered on the face-selective frequency and its three additional
harmonics (i.e., 1.2 Hz, 2.4 Hz, 3.6 Hz, and 4.8 Hz), as well as on the base
stimulation frequency and its harmonics (i.e., 6 Hz, 12 Hz, and 24 Hz). The
amplitude of these FFT segments was summed and transformed into a Z-score. Z-
scores were computed as the difference between the amplitude at the target
frequency bin and the mean amplitude of 20 surrounding bins (10 on each side)
divided by the standard deviation of amplitudes in the corresponding 20 bins. SNR
spectra were also calculated as the ratio between the amplitude at each frequency
bin and the average of the corresponding 20 surrounding bins (11 on each side,
excluding the 2 bins directly adjacent to the bin of interest). A cluster (SU or MU) was
considered as showing a significant response for faces and classified as "face-
selective" if the Z-score at the target frequency bin exceeded 1.64 (i.e., p < 0.05 one-
tailed). In addition, face-selectivity was characterized at a more conservative
threshold (i.e., Z > 2.32, p < 0.01). To isolate face-selective responses from
responses to non-face objects in the time domain (as in Fig. 2C, F), an FFT notch
filter (filter width = 0.05 Hz) was then applied on the 70 s single or multi-units time-
series to remove the general visual response at 6 Hz and two additional harmonics
(i.e., 12 and 18 Hz). To account for the sinusoidal modulation of contrast, the face
onset time was shifted forward by 33 ms (~1/5 of a 6 Hz cycle duration). This delay
was estimated by comparing SEEG responses to sequences presented with
sinewave or squarewave (i.e., abrupt) contrast modulation of visual stimulation. The
onset of face-selective response was delayed by 30-35 ms for sinusoidal visual
stimulation, which corresponds to 4 screen refresh frames (33 ms) and 35% of the
full contrast. The spike trains of each identified cluster were then segmented into 1-s
segments around face onset, and the resulting epochs were temporally smoothed
(20 ms time-window) and averaged. The net average spike rate was calculated by
subtracting each sequence's mean baseline spike rate in a [-0.166–0 s] time-window
relative to face onset. Finally, the latency was computed as the time point at which
net firing crossed the baseline +/- 2.58 s.d. value (i.e., p < 0.01, two-tailed percentile
bootstrap) for at least 30 ms. as described in Jacques et al., 2022.

**FMRI acquisition.** The two participants were scanned at the CHRU-Nancy, with a 3T Siemens Magnetom Prisma system (Siemens Medical System, Erlangen, Germany) with a 64-channel head-neck coil. Anatomical images were collected
179 using a high-resolution T1-weighted magnetization-prepared gradient-echo image
180 (MP-RAGE) sequence (192 sagittal slices, TR = 2300 ms, TE = 2.6 ms; flip angle
181 (FA) = 9°, field of view (FOV) = 256 × 256). Functional images were collected with a
182 T2*-weighted simultaneous multi-slice echo planar imaging (SMS EPI) sequence
183 (TR = 1500 ms, TE = 30 ms, FA = 72°, FOV= 240×240 mm², voxel size = 2.5 mm
184 isotropic, matrix size = 96×96 for sequences done in 2019 or matrix size = 80x80 for
185 sequences done in 2022, interleaved), which acquired 44 oblique-axial slices
186 covering the entire temporal and occipital lobes. The total duration of each sequence
187 (run) for P1 (tested in 2019) was 333 s including 9 s of dummy scans (222 TRs). The
188 total duration of each run for P2 (tested in 2022) was 405 s including 9 s of dummy
189 scans (270 TRs). Images were back-projected onto a projection screen by an MRI-
190 compatible LCD projector. The participants observed the sequences through a mirror
191 placed within the FR head coil. The images subtended a viewing angle of 8° × 8°
192 (33.4 cm × 33.4 cm) at a viewing distance of 240 cm. Three fMRI sequences were
193 performed for each patient, who were scanned 6-8 weeks after the microelectrode
194 recordings.

195 **FMRI analyses.** As in Laurent et al. (2023), the volumes of each run were first
196 rigidly realigned with each other, and a mean functional image of the runs was
197 computed for co-registration with the anatomical image, using *SPM12*. The volumes
198 were also spatially smoothed with a Gaussian kernel of 2 mm (FWHM; i.e., full width
199 at half maximum). The functional runs were averaged across runs in the volumetric
200 space. Then, a Fourier analysis was performed using the FFT function in Matlab
201 without windowing. The FFT was applied on the entire BOLD response time-course
202 and the amplitude spectrum was directly derived from the Fourier transform
203 coefficients. Amplitudes at the face stimulation frequency (i.e., at F = 0.111 Hz) were
204 converted into Z-scores, using the mean and standard deviation of the amplitude at
205 neighboring frequencies (see Gao et al., 2018 for details). The relative activations
206 and deactivations of the neural responses at the face stimulation frequency were
207 defined by the phase of the BOLD response. For each individual, the histogram (20
208 bins) of phase values of all the voxels with a Z-score > 3.1 and with a positive phase
209 value was calculated. The phase value of the histogram bin that has the largest
210 number was used as the center phase (φ) to define all the voxels with their phase
211 values within φ ± π/2 as activations (+ sign) and voxels with their phase values
212

outside of this window as deactivations (– sign). These signs were then applied to Z-score maps to obtain the final response map.

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214

## Results

215

In both MidFG regions sampled, robust differential activity to natural images of faces vs. non-face categories was objectively recorded with a similar well-validated frequency-tagging paradigm in fMRI (Fig. 1A) and microelectrode electrophysiological recordings (Fig. 1B).

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<figure><img src="image_3.png" /></figure>

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**Fig. 1.** The frequency-tagging Face Localizer paradigm in fMRI (Gao et al., 2018) and intracerebral electrophysiological recordings (Jonas et al., 2016). In both cases, the same variable natural non-face images alternate at a 6 Hz rate (1 fixation/image). (A) During a 405 s fMRI run, a “mini-burst” of 7 images of variable faces (purple: “F”) alternating with 6 non-face object images (orange: “O”) is presented every 9 s (0.111 Hz). A full run is composed of 44 cycles (3 s shown here). (B) During intracerebral recordings, variable face images are inserted periodically every fifth image (1.2 Hz). Each recording includes two 70-s stimulation sequences (3 s shown here). With both recording methods (and EEG; Rossion et al., 2015), this paradigm provides robust population-level face-selective activity devoid of low-level sensory confounds at the tagged frequencies. Here it is applied to single and multi-unit recording activity in the human fusiform gyrus.

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230 FMRI category-selective responses

231 **Fig. 2A and D** illustrate the category-selective regions identified with fMRI, on
232 axial, coronal, and sagittal slices in both participants ($Z > 3.1$; $p < 0.001$), relative to
233 the microelectrodes location. Fast Fourier Transform (FFT) of the BOLD response
234 time-courses is used to define face-selective voxels in the frequency-domain (0.111
235 Hz; Gao et al., 2018). BOLD frequency spectra at each voxel are transformed into $Z$-
236 scores, which can either be positive (higher response to face than non-face stimuli,
237 i.e., BOLD signal increase) or negative (lower response to faces than non-faces, i.e.,
238 BOLD signal decrease).

<figure><img src="image_4.png" /></figure>

239

240 **Fig. 2. Relationship between BOLD signal and neuronal activity in the human MidFG. (A), (D)**
241 Significant face-selective activations (hot colors) and deactivations (cold colors) on axial, coronal, and
242 sagittal slices ($Z > 3.1$; $p < 0.001$). The estimated location of the microelectrode (green circle) falls in
243 left MidFG activation in P1 and right MidFG deactivation in P2. **(B), (E)** Representative raster plots of
244 two face-selective single-units (SU) showing activity increase in P1 and decrease in P2 to faces,
245 respectively. The representative face-selective SU in panel B also exhibits a high increase in firing
246 rate to the general visual stimulation at 6 Hz. Each line corresponds to a 1 s epoch time-locked to the
247 onset of a face (at 0 s), from a 140 s of recording. SU waveforms are shown in the upper-right
248 corners. **(C), (F)** Average time-courses of all face-selective SU identified ($N = 45$ across 4 sessions in
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P1, *N* = 99 across 7 sessions in P2; Z > 2.32, *p* < 0.01), showing response increase to faces in P1 and decrease to faces in P2.

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As expected, faces elicited larger BOLD responses than non-face objects in the lateral MidFG, but lower responses in the medial MidFG (Pelphrey et al., 2003; Gao et al., 2018). Microwires in P1 (estimated Talairach location -40, -46, -19; **Fig. 2A**) fell in the lateral MidFG face-selective region: the Fusiform Face Area ("FFA", peak coordinate: -35, -51, -18) (Quiroga et al., 2023), while microwires in P2 (29, -41, -12; **Fig. 2D**) located in the medial MidFG, an unprecedented sampled region with lower (deactivated) BOLD signal for faces.

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Cellular category-selective responses

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Recordings of category-selective spiking activity in P1 and P2 took place over up to 11 days, with single-units detected on 7 microwires per session. Across all independent sessions (i.e., on different half-days), we recorded 245 units (*N* = 206 single-units, SU and *N* = 39 multi-units, MU; pooled over 4 sessions for P1 and 7 sessions for P2) firing to variable pictures of faces and objects. Following FFT, common visual responses to faces and objects were identified at the base frequency rate and harmonics (6, 12, and 18 Hz) while face-selective responses were measured at the specific face-stimulation frequency and harmonics (i.e., 1.2, 2.4, 3.6, and 4.8 Hz; **Fig. 1B**) (Rossion et al., 2015; Jonas et al., 2016). Despite a brief recording time for each unit (2 sequences of 70 s), only a small proportion of single neurons failed to respond (i.e., no significant base rate response: *N* = 10/62, 16.1% in P1; *N* = 17/144, 11.8% in P2; all Z < 1.64, all *p*<sub>s</sub> < 0.05; no difference between participants, *p* = 0.54, Pearson's χ<sup>2</sup>). Among all visually responsive neurons, we found a very high proportion of face-selective neurons (*p* < 0.05) in both activated and deactivated MidFG regions (P1: 98.1%; *N* = 51/52; P2: 86.6%; *N* = 110/127). Even at a more conservative threshold (*p* < 0.01), strong face-selectivity is observed (P1: 86.5%; *N* = 45/52; P2: 77.9%; *N* = 99/127; no difference between the two individuals, *p* = 0.27, Pearson's χ<sup>2</sup> test).

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We then determined whether face-selective single neurons (*p* < 0.01) recorded in activated or deactivated face-selective regions exhibited different types of responses to faces relative to non-face objects. While a large majority of face-selective neurons in the lateral MidFG (P1) showed a significant relative increase in
spike rate to faces (88.9%; N = 40/45; **Fig. 2B, C**), most neurons in the medial
MidFG (P2) showed significant relative spike rate decrease to faces (95.9%; N =
95/99; **Fig. 2E, F**). While the mean onset response latency did not differ between
increases and decreases (118.2 ms vs. 127.3 ms, respectively; *p* > 0.05, two-tailed
percentile bootstrap; **Fig. 2C, F**), a significantly lower response (i.e., absolute
average firing rate) was found for decreases than increases ([130-300 ms]; 3.69
spikes/sec for increases, ranging from 0.99 to 7.42 spikes/sec; -2.73 spikes/sec for
decreases, ranging from -0.44 to -9.67 spikes/sec; t-test *p* < 0.01). A final noteworthy
observation is that, among the 95 single-units showing a reduced firing rate to faces
(P2), 34 lacked a significant general visual response (at 6 Hz; i.e., 34,3%; *p* < 0.05).
Beyond a mere relative reduction to faces compared to non-face objects, responses
of such "face-exclusive" single neurons therefore appear to reflect genuine
suppression of baseline spiking activity upon presentation of a face.
294 Discussion

295 Although BOLD signals correlate better with local field potentials (LFP) than
296 spikes on a trial-by-trial basis (Logothetis et al., 2001), positive correlations have
297 been observed between SU and BOLD in human (Mukamel et al., 2005; Nir et al.,
298 2007) and non-human primates (Goense and Logothetis, 2008). Here thanks to a
299 unique combination of fMRI and microelectrode recordings in the same individual
300 human brains, we report a striking correspondence between increases/decreases in
301 BOLD activity and (SU) neuronal firing, in two neighboring areas of the human
302 association cortex. By showing that the sign of the BOLD signal fluctuation during
303 visual stimulation matches the sign of SU activity, our findings shed light on the
304 physiology of BOLD signals, revealing in particular that BOLD decreases can be due
305 to relative, but also absolute, spike suppression in the human brain. Such transient
306 spikes suppression could be due to suppression/reduction of synaptic inputs to the
307 recorded neuron(s) or to inhibitory inputs to the neuron(s).

308 Up to now, the cellular basis of the relative BOLD decrease to faces in the
309 medial portion of the fusiform gyrus, attributed to a smaller increase to faces relative
310 to non-face objects or an absence of response to faces (Kanwisher, 2017), has
311 remained somewhat of a mystery. Importantly, due to both the lack of a fusiform
312 gyrus in non-hominoid primates (Weiner and Zilles, 2016) and of reported BOLD
313 deactivation to faces in the monkey brain, single-unit recordings in macaque
314 monkeys' face-selective cortical regions (Tsao et al., 2006; Bell et al., 2011) could
315 not address this issue. Here, our neurophysiological recordings in humans suggest
316 that faces do not only elicit a strong decrease of neuronal activity relative to non-face
317 objects in the medial MidFG, but a genuine suppression of baseline spiking activity in
318 a substantial proportion of the units recorded. Thus, beyond contributing to clarify the
319 physiological basis of the fMRI signal, our study shed light on the nature of face-
320 selective neural activity in the human brain, which can be expressed both as
321 increase and active suppression of spiking activity. Inhibitory cellular activity is
322 thought to play an important balancing role to maintain stable function of cortical
323 circuits (Vogels et al., 2011), and has been shown to play a key role in cortical
324 category-selectivity (Wang et al., 2000). Further direct recordings in the human
325

association cortex will be necessary to determine the functional relevance (i.e.,
relationship to visual recognition behavior) and the cellular mechanisms (linked to
cyto- and myelo-architecture; Lorenz et al., 2017) underlying these category-
selective (excitatory and) inhibitory neuronal activities.

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329

Acknowledgments

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We thank the two participants for their involvement in the study. This research was
supported by the ERC Adg HumanFace 101055175 awarded to Bruno Rosson and
a PhD fellowship from the Université de Lorraine to Marie-Alphée Laurent.

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