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Psychiatry Investig > Volume 21(10); 2024 > Article
Kim, Jo, Kwon, Park, Han, and Kim: Association of Compensatory Mechanisms in Prefrontal Cortex and Impaired Anatomical Correlates in Semantic Verbal Fluency: A Functional Near-Infrared Spectroscopy Study

Abstract

Objective

Semantic verbal fluency (SVF) engages cognitive functions such as executive function, mental flexibility, and semantic memory. Left frontal and temporal lobes, particularly the left inferior frontal gyrus (IFG), are crucial for SVF. This study investigates SVF and associated neural processing in older adults with mild SVF impairment and the relationship between structural abnormalities in the left IFG and functional activation during SVF in those individuals.

Methods

Fifty-four elderly individuals with modest level of mild cognitive impairment whose global cognition were preserved to normal but exhibited mild SVF impairment were participated. Prefrontal oxyhemoglobin (HbO2) activation and frontal cortical thickness were collected from the participants using functional near-infrared spectroscopy (fNIRS) and brain MRI, respectively. We calculated the β coefficient of HbO2 activation induced by tasks, and performed correlation analysis between SVF induced HbO2 activation and cortical thickness in frontal areas.

Results

We observed increased prefrontal activation during SVF task compared to the resting and control task. The activation distinct to SVF was identified in the midline superior and left superior prefrontal regions (p<0.05). Correlation analysis revealed an inverse relationship between SVF-specific activation and cortical thickness in the left IFG, particularly in pars triangularis (r(54)=-0.304, p=0.025).

Conclusion

The study contributes to understanding the relationship between reduced cortical thickness in left IFG and increased functional activity in cognitively normal individuals with mild SVF impairment, providing implications on potential compensatory mechanisms for cognitive preservation.

INTRODUCTION

Semantic verbal fluency (SVF) is a type of cognitive performance to measure verbal ability and other accompanying cognitive processes. Including a language ability which involves the production of words, SVF utilizes various types of cognitive abilities such as executive function to select the appropriate words related to specific categories, mental flexibility to switch between categories, and semantic memory function to retrieve semantic information [1,2]. These abilities are frequently impaired in the early stage of dementia [3]. Raoux and colleagues reported that switching scores were reduced in future Alzheimer’s diseases (AD) patients and kept declining during preclinical phase of AD [4]. Clark and colleagues [5] proposed that a decreased ability to switch between semantic categories may foretell later global cognitive decline in non-demented older adults as well. Consequently, it is postulated that a disrupted SVF network can predict impairment in global cognitive functioning. Indeed, the test for SVF is widely regarded as a reliable tool for screening dementia including AD [3,6]. In this regard, identifying declines in SVF could aid in the early diagnosis of AD.
From neuroimaging studies with healthy individuals, anatomical correlates underlying SVF have been mostly observed in left frontal and left temporal lobe. Left frontal lobe as a whole works as a crucial role in SVF, but the role of left inferior frontal gyrus (IFG) was more specifically and consistently demonstrated as it directly reflects the semantic-related cognitive process compared to the other SVF associated frontal regions [7-9]. Regarding the specific role of left frontal inferior gyrus, it involves in semantic processing both during language comprehension and production as well as selection of semantic knowledge [1,10]. Retrieval and storage of semantic knowledge are suggested to be the roles of the left temporal regions [7]. Accordingly, neural correlates of SVF also have been investigated extensively using neuroimaging techniques like functional magnetic resonance imaging method (fMRI) or functional near-infrared spectroscopy (fNIRS). SVF has been shown to reliably elicit bilateral functional hemodynamic responses in frontal lobe but pronounced left hemisphere within middle frontal gyri, IFG, fronto-temporal regions, and left temporal gyrus in cognitively normal adults [1,8,9,11].
Previous structural neuroimaging studies of individuals with ischemic stroke or traumatic brain injury who had a direct lesion in the left hemisphere have reported that structural abnormalities were associated with SVF deficits [12-14]. Further, impaired SVF in aged adults and AD patients was in commonly related to the bilateral cerebral atrophy besides the atrophy in the major correlates of SVF, and in the extra parts of cerebral regions (i.e., Parietal cortices) [15-17]. Along with these findings, recent several studies with fNIRS have found that individuals with impaired SVF, such as mild cognitive impairment (MCI) or AD showed a shared abnormal pattern of prefrontal activation during the SVF task (SVFT), which is loss of left lateralization [18-21]. There were also different patterns of prefrontal activation during the SVFT from to the normal individuals with no SVF impairment and from to each clinical group. In the individuals with AD, who have advanced cognitive impairment, prefrontal regions were less activated during the SVFT in both hemispheres than in cognitively normal individuals [18,20,22]. This reduced activation in bilateral frontal lobes caused absence of lateralization to the left hemisphere. However, in those with mild SVF impairment, left IFG and other prefrontal regions related to SVF were hypo-activated while increasing the hemodynamic activity in dorsolateral prefrontal cortex in either right or left hemisphere than in cognitively normal individuals [19,21,23]. This suggested that the inefficient utilization of primary neural network due to neuronal loss may be compensated by activation of other non-dominant to SVF regions in prefrontal cortices in early stage of cognitive impairment [24-26].
Yet, Price and Friston [27] reported that the capacity to compensate in neural resources is limited and the ability to manage the resources were inversely proportional to the severity of cognitive impairment. As neural damage progresses, the decline in both cognitive processing efficiency and capacity hinders neuronal recruitment and diminishes compensation capabilities, leading to overall poorer performance. Additionally, the presence of lateralization and compensatory activation is notably found in the frontal lobes, rather than in the temporal lobes in overall span of cognition. Therefore, individuals with modestly impaired SVF but no noticeable cognitive decline have ability to compensate for the effects of neuronal loss in the IFG areas by recruiting additional neural resources to superior frontal areas as substitutes for performing the SVFT. However, no study to date has directly demonstrated an association between the degree of neuronal loss in the SVF-dominant region in prefrontal cortex (IFG) and the level of functional activation in the dorsolateral prefrontal cortex during the SVFT in the individuals with cognitively normal but subtle SVF deficits.
The aims of this study are 1) to determine the pattern of prefrontal activation during the SVFT. We chose prefrontal regions because the presence of lateralization and compensatory activation is known to be notably found in the frontal lobes, rather than in the temporal lobes regardless of cognitive decline level [28] and 2) to examine the influence of cortical thickness in the SVF dominant prefrontal cortex and the level of prefrontal activation during the SVFT in individuals with mild level SVF impairment in cognitively normal individuals.

METHODS

Participants

We enrolled 54 elderly Koreans aged 60 years or older who were diagnosed with MCI with a clinical dementia rating (CDR) of 0 or 0.5 (22 men and 42 women; age, 71.6±5.4 years). The following conditions were strictly excluded: inability to read or speak Korean; visual or hearing impairment; major psychiatric or neurological disorders including dementia, mood disorders, and cerebrovascular disease, severe physical conditions that may affect cognitive function, and use of cognitive enhancers or neuroprotective drug. They were patients attending the dementia clinic at Seoul National University Bundang Hospital (SNUBH) or participants of the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD) [29]. The KLOSCAD is a nationwide, multicenter, prospective cohort study on 6,818 community-dwelling Koreans aged 60 years or older randomly sampled from the residents of 30 districts across South Korea, with follow-up assessments every 2 years from November 2010 to October 2020.
Geriatric psychiatrists evaluated standardized face-to-face diagnostic interviews and physical and neurological examinations using the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K) Clinical Assessment Battery [30] and the Korean version of the Mini International Neuropsychiatric Interview [31]. Laboratory tests, including complete blood counts, chemistry profiles, and serologic tests for syphilis, were also performed for each participant. Neuropsychologists or trained research nurses administered the CERAD-K Neuropsychological Assessment Battery which consists of the following neuropsychological tests: Semantic Verbal Fluency Test, 15-item Boston Naming Test, Word List Memory Test, Constructional Praxis Test, Word List Recall Test, Word List Recognition Test, Trail Making Test A/B, Digit Span Test, and Frontal Assessment Battery [32-34]. A panel of research neuropsychiatrists determined the diagnosis and CDR of each participant at the consensus diagnostic conference [35]. We diagnosed MCI according to the revised diagnostic criteria for MCI proposed by the International Working Group on MCI [36].
All participants gave written informed consent, either themselves or through their legal guardians. This study was approved by the Institutional Review Board of SNUBH (IRB No. B-2005-/615-302).

Structural brain MRI

Participants underwent MRI within 1 year of the date of fNIRS. We acquired three-dimensional structural T1-weighted spoiled gradient echo magnetic resonance images of the participants using a 3.0 Tesla Achieva scanner (Philips Medical Systems; Eindhoven, The Netherlands) at SNUBH. The images were acquired using the following parameters: voxel size of 1.0×0.5×0.5 mm3, sagittal slice thickness of 1.0 mm with no interslice gap, echo time of 4.6 ms, repetition time of 8.1 ms, number of excitations of 1, flip angle of 8°, field of view of 240×240 mm and 175×240×240 matrix in x, y, and z dimensions. We converted the original Digital Imaging and Communications in Medicine (DICOM) format images to NIfTI format images using MRIcron software (https://www.nitrc.org/projects/mricron). We bias-corrected the T1 images to remove intensity inhomogeneity artifacts using Statistical Parametric Mapping software (version 8, SPM8; Wellcome Trust Centre for Neuroimaging, London, UK; http://www.fil.ion.ucl.ac.uk/spm) [37,38]. We then resliced the bias-corrected T1 images into isotropic voxels (1.0×1.0×1.0 mm3).
We then automatically segmented whole brain structures to extract cortical thickness using recon-all streams from FreeSurfer version 6.0 (http://surfer.nmr.mgh.harvard.edu) as defined by the Desikan-Killiany-Tourville (DKT) atlas [39]. The reconstruction procedure consists of three steps. In the first step, it performs motion correction, non-uniform intensity normalization, and skull stripping. In the second step, it performs full volumetric labeling with automatic topology fixing. In the final step, it performs spherical mapping and cortical parcellation. After the recon-all process, we obtained cortical thickness values from parcellated individual frontal brain masks of regions of interest (ROIs) from left IFG regions which consisted of pars-orbitalis, pars-triangularis, and pars-opercularis according to the DKT atlas using FreeSurfer version 6.0 [40].

Task paradigm

Participants were instructed to sit on a comfortable chair and to avoid movement as much as possible. This study consisted of 3 sessions with resting phases for recording resting-state Blood-Oxygen-Level-Dependent (BOLD) signals and task phases of each task, a SVFT and control task for recording task-elicited BOLD signals (Figure 1A). There were around 15 s of intervals between each session for reading the instruction displayed on a monitor for next session. A session consisted of 4 blocks. There also were a several seconds of interval less around 10 s between each block for reading the instruction displayed on a monitor for next block. Each block is equivalent to 2 periods of resting, one SVFT, and one control task. A resting period was assigned before and after the SVFT, and the control task was assigned after the second resting period ended. In this order, a session started with resting and ended with the control task. Resting-state BOLD signals were recorded during each resting period for 30 s, respectively while the participants stayed still their eyes open fixing on a fixation cross symbol displayed on a monitor. The task-elicited BOLD signals were recorded during a SVFT and control task for 30 s each. In each session, the entire BOLD signals were recorded for 120 s. Three experimental sessions were 6 min (instruction time is not included).
SVFT had different categories for a block in each session: animals, vegetables, market items. The control had the same simple category for a block in every session, weekdays (Figure 1A). During the SVFT, participants had to generate as many words that related to the given semantic category as possible within 30 s. The task measures how much information can be retrieved from the categorization and memory repository of text. During the control task, participants had to slowly produce and repeat the names of weekdays for 30 s. Control task was designed to compare the induced brain activation by the control task and the SVFT, the task which requires a higher level of cognition and manifests semantic knowledge and fluency. Resting was performed to establish participants’ baseline brain activation.

fNIRS

We recorded the changes in prefrontal hemodynamic activity with BOLD signals over the experiment with a continuous wave system 27-channel Brite24 (Artinis Medical Systems B.V., Elst, The Netherlands). Changes in the concentrations of oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR) across the channels were recorded in the units of micromolar-millimeter (μM×mm) at 10 Hz of sampling rate by 2 near infrared wavelengths, 760 nm and 850 nm, respectively.
We utilized the fNIRS device consists of 10 light transmitters and 8 detectors with a 3.0 cm of inter-optode distance, and the fNIRS data were collected at 27 measurement channels located between each transmitter and detector (Figure 1B). The device was adjusted based on landmarks from the International 10-20 system. Specifically, for the most superior side of the probeset, channel 16 and 17 were positioned between the marker FCz and Fz, and 16 to the right and 17 to the left. Moreover, for the most inferior side of the probeset, the corresponding channels were positioned between the FPz and AFz, and 6 to the right and 18 to the left. This spatial information was used to calculate projections of the superficial optode to the cortex and position channels onto a standard brain template in standard stereotactic brain coordinate systems of montreal neurological institute using AtlasViewerGUI, a visual graphical user interface contained within Homer2 (https://homer-fnirs.org/) [41,42]. Afterwards, label assignment for the corresponding channels was defined by the automated anatomical labeling (AAL) [43], and fNIRS channels recorded BOLD signals within the superior frontal and middle frontal cortex according to the AAL label assignment. Hereby, the ROI on the right superior frontal cortex covered channels #5, 6, 7, 8, 9, 10, 11, and 16. Similarly, the ROI on the left superior frontal cortex covered channels #14, 17, 18, 19, 21, 22, 23, and 24. The ROI on the right middle frontal cortex covered channels #1, 2, 3, and 4, and the left middle frontal cortex covered channels #20, 25, 26, and 27. The ROI on the midline superior frontal regions covered channels #12, 13, and 15 (Figure 1B).
The fNIRS data were preprocessed using the open source packages, MNE-Python version 1.6 (www.martinos.org/mne/stable/index.html) and MNE-NIRS version 0.5.0 (https://mne.tools/mne-nirs/stable/index.html). The optical intensity signals were first transformed into the time series of HbO2 and HbR concentration changes using the modified Beer-Lambert law [44]. A bandpass filter was applied to converted signals with cutoff frequencies of 0.01 Hz and 0.09 Hz to remove slow drifts and high frequency fluctuations of motion and physiological noise [45-47]. We investigated only the HbO2 data as validated BOLD signals in the present study because HbO2 offers better signal-to-noise ratio and demonstrates a stronger relationship with the BOLD compared to HbR, and thus has better reliability and sensitivity to verbal-related changes in cerebral blood flow [48-50].
After preprocessing, we used task blocks as epochs for analyzing HbO2 concentration changes during the resting state and tasks. We obtained 6 epochs from resting state HbO2 concentration and 3 epochs from SVFT and control task, respectively. Each epoch had the same length of a task block, which is 30 s. To find the HbO2 concentration changes related to each task and resting state, we calculated weighted averaging for HbO2 concentration across time points of epochs in tasks and a resting state by block averaging. Block-averaged HbO2 concentration in each epoch was then corrected its baseline to a zero. The baseline-corrected data were then averaged across channels, and participants. Previous fNIRS studies have suggested a lag in hemodynamic activity like earliest activation starting from 5 s after task onset and sharp activation at around 5-10 s after onset [45,51]. Therefore, to observe the level of activation in a full length as possible, we included the time course of 10 s after the epoch was ended. The purpose for this analyzing strategy was to observe any visual differences among the change of HbO2 concentrations of resting state, control task, and SVFT (Figure 2) not to assess the response value to the task. Instead, we utilized the general linear model (a model-based statistical analysis tool) to identify hemodynamic responses to the tasks because the estimation of the response tends to be more accurate and robust compared to the block-averaging technique as it can derive hemodynamic response function (HRF) considering the entire time course of HbO2 fluctuation in addition to the task and resting periods [52,53]. The HRF is used to serve as a reference to estimate the changes in HbO2 signals during the task [54]. The formula is as follows:
(1)
Y=Xβ+ε,
(2)
X=h(t)s(t)
where Y represents the temporal profile of the measured HbO2, β is the estimated amplitude of the changes in HbO2, and ε represents the residual owing to the difference between the measured signals and the predicted model. X is the stimulation-specific predicated response, which is expected to match the temporal profiles of the measured hemodynamic signal, HRF; h(t) represent the canonical HRF, and s(t) is the stimulation-specific boxcar function for a given task. A convolution matrix of h(t) and s(t) provides the HRF. Fitting the equation (2) calculates β, statistical t-value representing the statistical significance of the changes in HbO2 (HRF) with respect to the baseline at each respective channel [55]. We modeled the baseline drift with a 2nd order polynomial. After that, we obtained the group-level β values at each channel which were averaged across participants by group-level hemodynamic analyses. All analyses were performed using the MNE-Python version 1.6 and MNE-NIRS version 0.5.0 [56].

Statistical analysis

We compared HRF β coefficients at all channels between the resting block, SVFT block, and control task block using one-way repeated measures analysis of variance (rmANOVA) with the Greenhouse-Geisser non-sphericity correction and Bonferroni post hoc comparisons.
Subsequently, we identified the distinguished semantic verbal ability-specific (SVA-S) HbO2 activation from channels with statistically greater HRF level (β) of the SVFT than that of resting as well as control task, using the Bonferroni post hoc analysis.
Afterwards, we examined the associations of SVA-S HbO2 activation on frontal regions with frontal cortical thickness of left IFG. We used the β coefficient of SVA-S HRF and cortical thickness data from structural MRI. To examine whether cortical atrophy in IFG areas can influence the HbO2 of SVA-S activation, we used two-tailed Pearson’s correlation analyses. All statistical analyses were performed utilizing SPSS 22.0 Software (IBM Corp., Armonk, NY, USA). The significance level was set at 0.05 for all tests.

RESULTS

Their mean age and educational level of the 54 participants was 71.6±5.4 years (range: 60-80) and 12.3±3.7 years (range: 6-18), respectively. About two thirds of them (65.6%) were women. The neuropsychological characteristics of the participants are summarized in Table 1. All participants were diagnosed with MCI and scored -1.5 standard deviation or less on one or more cognitive tests compared with age-, sex-, and education-adjusted normative data of cognitively normal Korean elderly. Considering that the SVF test z scores of the participants ranged from -2.57 to 1.64, the SVF of the participants ranged from moderately impaired to normal.
As shown in Figure 2, the block-averaged concentration change of HbO2 was greatest in the SVFT phase, followed by the control task phase. During each task phase, HbO2 began to increase about 10 seconds after the start of the task and began to decrease about 10 seconds after the end of the task because of delayed hemodynamic response [45,51].
As summarized in Table 2, the main effect of task was significant in 23 channels by rmANOVA. In post hoc comparisons, the averaged change in β coefficient of HRF in the SVFT phase was significantly greater than that in resting phase in 21 channels, and greater than both the resting and control task phases on 5 channels (3 channels in left superior frontal regions [channel 17, 21, and 24] and 2 channels on midline superior frontal regions [channel 13 and 15]). These 5 channels were categorized as channels with SVA-S HbO2 as they showed the greater level of SVFT HRF β coefficient compared to the other tasks.
As shown in Table 3, SVA-S HbO2 activation, the difference of HRF β coefficient between that of SVFT and control task, in these 5 channels in superior frontal cortex was inversely correlated with cortical thicknesses of IFG areas, pars triangularis and pars opercularis. The SVA-S HbO2 activation in channel 13, 15, and 24 was also inversely correlated with cortical thickness of pars orbitalis. The correlation was statistically significant between channel 17 and pars triangularis’ thickness (r(54)=-0.304, p=0.025).

DISCUSSION

In the current study, we examined the neural processing during the SVFT and neural activation specific to semantic verbal ability in individuals with mild SVF impairment using fNIRS. We also explored the association between left IFC and function in prefrontal areas which are known to be working for SVF, combining the HbO2 activation solely reflects the SVF and MRI data of grey matter cortical thickness in those individuals.
We targeted the individuals who have normal or similar to normal but have objectively mild impairment in SVF as we intended to find the response of neural utilization in the presence of the atrophy of SVF-related regions. As described in Table 1, the results of neuropsychological assessments clearly support that the group represents the older adults with almost no cognitive decline but mildly declined SVF. Therefore, we can be reasonably confident that outcomes from our data demonstrated the characteristics of target population, normal individuals with mild deficit of SVF.
Our findings suggested can be summarized as follows: 1) individuals with normal cognition and mild SVF impairment showed the prefrontal activation during SVFT. The activation level was higher than that of resting state, and that of the control task, which was to repeatedly produce the name of weekdays. The averaged concentration changes of HbO2 during the tasks showed that the SVF the highest, followed by control task, and resting (Figure 2). 2) As shown in Table 2, participants showed a difference in β coefficient of HRF in activated regions between the task conditions, i) bilateral activation on the entire prefrontal regions during SVFT compared to the resting state and ii) activation exclusive to SVF (SVA-S activation) on the midline and left superior areas in prefrontal cortex. 3) SVA-S activation of those areas are influenced by reduced left pars-triangularis thickness in the individuals with modest level of impairment in SVF.
Numerous previous studies have demonstrated that healthy controls or younger adults exhibit an elevation in HbO2 activation corresponding to increased cognitive demands, demonstrating efficient utilization of neural resources when engaging in more challenging tasks [57-59]. In line with this, our findings, illustrating an increase in HbO2 from a control task to the SVFT, suggest that participants in our study can enhance HbO2 levels in response to escalating cognitive demands. Two possible explanations can be considered. Firstly, individuals in the early stages of cognitive decline may still possess the capacity to discriminate cognitive levels between the repetition of ordinary words (weekdays) and the generation of words within specific categories, effectively deploying neural resources accordingly. Alternatively, participants may have found the SVFT relatively manageable, allowing for the effective utilization of a slightly reduced number of neurons to meet the increased cognitive demand.
Furtermore, we successfully confirmed the HbO2 activation specifically associated with SVF, referred to here as SVA-S activation. Even though we observed the hemodynamic activity during the SVFT, we believed that this activation did not purely reflect the response to semantic verbal ability. To identify the ‘true’ HbO2 activation related to semantic fluency, we subtracted the effect of word generation, which lacks semantic knowledge and other traits of semantic fluency such as mental flexibility or semantic memory retrieval, using HbO2 activation during the control task. While many functional studies using fNIRS or fMRI have reported HbO2 activation levels during the SVFT compared to the non-task (resting) phase [18,20,23], we eliminated potential confounding effects and obtained results expected to be genuinely elicited by the task.
Consequently, the activation pattern identified in the SVA-S activation suggests that the midline superior and left superior prefrontal regions may represent the neural correlates underlying semantic fluency in individuals with mild SVF deficits (Table 2). In contrast to the semantic fluency correlates in healthy younger adults, where left inferior, medial, and middle frontal regions are involved [8,9,11], our finding diverge as ours, channel #13, 15, 17, 21, 24 are associated with superior frontal regions. Herrmann et al. [60] reported reduced activation in IFG during the verbal fluency task in elderly subjects compared to younger individuals. Heinzel and colleagues [61] also demonstrated that age negatively correlates with activation in the IFG and positively correlates with dorsolateral frontal activation. In this context, the regions with semantically working SVA-S activated undergo cortical reorganization as cognition declines.
Subsequently, we observed the association between prefrontal HbO2 SVA-S activation in channel #17 and reduced cortical thickness in pars-triangularis in the context of similar behavioral performance. The pars triangularis, the anterior-ventral parts (BA 45) of IFG, has consistently been linked to semantic linguistic processes, playing a role in the retrieval of words prompted by semantic cues [10,62,63]. Considering its role in semantic fluency, our correlation between pars-opercularis and SVA-S dorsolateral prefrontal HbO2 activation which is conjectured to reflect the genuine of semantic knowledge is highly reliable. Also, this hints that thinner cortices in anterior part of IFG, especially in pars-triangularis in our study, let recruiting supplementary neurons for processing semantic knowledge from other regions as compensatory mechanisms, where the activation in those regions initially not expected to be involved in the task. Park and Reuter-Lorenz [64] proposed the STAC (Scaffolding Theory of Aging and Cognition) which views this usage of additional neural resources in prefrontal cortex is an adaptive response that engages in compensatory scaffolding to the declined neural structures and function in order to execute a particular cognitive goal, SVFT in our study, as similar to or slightly worse than those with thicker cortices in left IFG. Therefore, this alternate response to the degradation of primary task network mitigates decline in cognition inferred to be occurred in the in older adults who are cognitively normal or comparable to normal.
This study has several limitations. First, the participants lack the control group to compare with. The absence of a normal SVF control group makes it challenging to compare the degree of abnormal activation and structure in our study participants against to those with normal SVF. Previous case-control studies comparing healthy controls with individuals with mildly impaired SVF, however, have demonstrated that SVF reliably elicits bilateral activation in the frontal lobe, with pronounced left middle frontal gyri and left IFG in healthy younger individuals. Conversely, older adults or those with MCI exhibited reduced activity in the left IFG and increased activity in the right middle frontal or left superior frontal cortices [8,19-21,23]. Although we did not collect data from healthy controls in our study, we observed an altered pattern of prefrontal activation that aligns with findings from studies comparing SVF impairment groups with healthy controls. In order to comprehensively investigate the extent of abnormality of clinical groups, future research should consider including a healthy control population.
In addition, the ROIs are limited. Due to the limitation of fNIRS measurements to the dorsolateral prefrontal regions, we could only examine the influence of the left IFG on superior frontal areas. Given that semantic fluency performance may engage regions outside the frontal areas [11,65], participants may display altered activations in other brain regions. Future studies will explore similar associations throughout other cortical and subcortical regions. Finally, this study did not include a connectivity analysis. Considering the local dysfunction in prefrontal brain areas associated with the physiological mechanisms underlying SVF impairment, it is crucial to emphasize the functional connectivity of these regions in individuals with impaired SVF. Future research should focus on investigating the network-level dynamics within the prefrontal areas to gain a deeper understanding of their role in SVF impairment.
Nevertheless, this study is significant in that it is the first, to our knowledge, to establish a relationship between grey matter thickness abnormalities in the prefrontal cortex, a region well-known to be associated with SVF, and abnormalities in functional activation related to SVF. Additionally, the study holds importance as it utilized grey matter as a measure, known to better reflect the BOLD signal compared to white matter [66-68] to find this relationship.
In conclusion, our study utilized fNIRS to investigate neural processing during the SVFT and the specific activation associated with semantic verbal ability in cognitively normal individuals with mildly impaired SVF. We also explored the intricate relationship between prefrontal structure and function, focusing on areas known to contribute to the processing of semantic knowledge. Notably, our findings suggest that individuals with normal cognition may sustain SVF ability through a recruitment of additional neuronal networks in left superior frontal regions to compensate for malfunctioning primary networks, pars-triangularis. This implies a potential adaptive strategy in the face of cognitive decline, allowing for the preservation of SVF ability even with a diminished number of neurons in the task network.

Notes

Availability of Data and Material

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

Ki Woong Kim, a contributing editor of the Psychiatry Investigation, was not involved in the editorial evaluation or decision to publish this article. All remaining authors have declared no conflicts of interest.

Author Contributions

Conceptualization: Hae-In Kim. Data curation: Hae-In Kim. Formal analysis: Hae-In Kim, Minjeong Kwon, Ji Eun Park. Funding acquisition: Ki Woong Kim. Investigation: Hae-In Kim. Methodology: Hae-In Kim, Ki Woong Kim. Project administration: Hae-In Kim, Ji Won Han, Ki Woong Kim. Resources: Ji Won Han, Ki Woong Kim. Software: Hae-In Kim, Sungman Jo. Supervision: Ki Woong Kim. Validation: Hae-Im Kim, Sungman Jo. Visualization: Hae-In Kim. Writing—original draft: Hae-In Kim. Writing—review & editing: Hae-In Kim, Ki Woong Kim.

Funding Statement

This work was supported by the National Research Council of Science & Technology grant by the Korea government (MSIP) (no. CRC-15-04-KIST).

ACKNOWLEDGEMENTS

None

Figure 1.
Experimental paradigm and fNIRS channels. A: Task paradigm for the fNIRS measurement. B: Configuration of the probeset with positions of the measured fNIRS channels located between each transmitter (blue-colored numbers) and detector (red-colored numbers) over the prefrontal cortex in accordance with the International 10-20 System. Boxes with orange colors indicate superior frontal regions in the left and right hemispheres and the ones with light orange indicate the midline superior frontal regions. Blue-colored boxes indicate rostral middle frontal cortices in the left and right hemispheres. SVFT, semantic verbal fluency task; Control, control task; fNIRS, functional near-infrared spectroscopy.
pi-2023-0447f1.jpg
Figure 2.
Group-averaged hemodynamic responses in resting (A), semantic verbal fluency task (B), and control task (C).
pi-2023-0447f2.jpg
Table 1.
Neuropsychological performance of the participants (N=54)
Raw score
Z score
Mean±SD Range Mean±SD Range
Mini Mental Status Examination 27.3±1.9 21-30 0.00±1.19 -5.00-1.00
Semantic Verbal Fluency Test 14.4±4.0 5-22 -0.17±1.02 -2.57-1.64
15-item Boston Naming Test 13.6±1.2 9-15 0.43±0.89 -2.00-2.00
Word List Memory Test 15.8±3.3 9-23 -0.26±1.04 -2.00-2.00
Word List Recall Test 4.15±1.8 0-9 -0.85±0.82 -2.00-1.00
Word List Recognition Test 7.77±2.1 0-10 -0.75±1.52 -6.00-1.00
Constructional Praxis Test 10.1±1.3 7-11 0.25±1.05 -3.00-1.00
Trail Making Test A 49.9±21.6 21-117 0.85±0.81 -2.00-2.00
Trail Making Test B 132.8±61.3 67-267 0.84±1.00 -1.51-1.92
Digit Span Test Forward 11.0±3.4 3-18 0.23±1.05 -2.00-3.00
Digit Span Test Backward 2.4±1.5 0-7 -0.08±1.04 -2.30-2.23
Frontal Assessment Battery 14.4±2.5 9-18 -0.17±1.30 -4.00-2.00

SD, standard deviation

Table 2.
Comparisons of hemodynamic responses in resting, control task, and semantic verbal fluency task phases
F* p* np2* Post hoc*
Right middle frontal
 1 9.63 <0.001 0.154 Resting<Control, SVFT
 2 3.65 0.029 0.065 Resting<SVFT
 3 10.41 <0.001 0.164 Resting<Control, SVFT
 4 9.84 <0.001 0.157 Resting<Control, SVFT
Left middle frontal
 20 10.98 <0.001 0.172 Resting<Control, SVFT
 25 9.46 <0.001 0.151 Resting<Control, SVFT
 26 9.08 <0.001 0.146 Resting<Control, SVFT
 27 4.41 0.016 0.077 Resting<SVFT
Midline frontal
 12 3.71 0.033 0.065 Resting<SVFT
 13 8.14 0.001 0.133 Resting, Control<SVFT
 15 14.05 <0.001 0.210 Resting<Control<SVFT
Right superior frontal
 5 3.17 0.046 0.056 -
 6 2.16 0.119 0.039 -
 7 2.38 0.106 0.043 -
 8 7.81 0.001 0.128 Resting<Control, SVFT
 9 2.13 0.123 0.039 -
 10 2.25 0.111 0.041 -
 11 4.38 0.015 0.076 Resting<SVFT
 16 5.50 0.005 0.094 Resting<Control, SVFT
Left superior frontal
 14 4.56 0.016 0.079 Resting<SVFT
 17 10.42 <0.001 0.164 Resting<Control<SVFT
 18 7.20 0.003 0.120 Resting<Control, SVFT
 19 7.07 0.002 0.118 Resting<SVFT
 21 7.72 0.001 0.127 Resting, Control<SVFT
 22 5.01 0.008 0.086 Resting<SVFT
 23 3.42 0.036 0.061 -
 24 6.16 0.003 0.104 Resting, Control<SVFT

* repeated measures analysis of variance with Bonferroni post hoc comparisons.

SVFT, semantic verbal fluency task

Table 3.
Correlations of left inferior frontal cortical thickness with hemodynamic responses that were higher in the semantic verbal fluency task phase compared to both resting and control task phases
Midline superior frontal
Left superior frontal
Channel 13
Channel 15
Channel 17
Channel 21
Channel 24
r* p* r* p* r* p* r* p* r* p*
Left pars orbitalis -0.041 0.771 -0.024 0.864 0.051 0.713 0.009 0.947 -0.073 0.599
Left pars triangularis -0.220 0.109 -0.241 0.079 -0.304 0.025 -0.169 0.222 -0.132 0.343
Left pars opercularis -0.146 0.293 -0.062 0.656 -0.089 0.520 -0.088 0.527 -0.091 0.514

* two-tailed Pearson’s correlation analysis;

cortical parcellation of thickness is based on the Desikan-Killiany atlas

REFERENCES

1. Costafreda SG, Fu CHY, Lee L, Everitt B, Brammer MJ, David AS. A systematic review and quantitative appraisal of fMRI studies of verbal fluency: role of the left inferior frontal gyrus. Hum Brain Mapp 2006;27:799-810.
crossref pmid pmc
2. Troyer AK, Moscovitch M, Winocur G. Clustering and switching as two components of verbal fluency: evidence from younger and older healthy adults. Neuropsychology 1997;11:138-146.
crossref pmid
3. Kwon SJ, Kim HS, Han JH, Bae JB, Han JW, Kim KW. Reliability and validity of Alzheimer’s disease screening with a semi-automated smartphone application using verbal fluency. Front Neurol 2021;12:684902
crossref pmid pmc
4. Raoux N, Amieva H, Le Goff M, Auriacombe S, Carcaillon L, Letenneur L, et al. Clustering and switching processes in semantic verbal fluency in the course of Alzheimer’s disease subjects: results from the PAQUID longitudinal study. Cortex 2008;44:1188-1196.
crossref pmid
5. Clark LR, Schiehser DM, Weissberger GH, Salmon DP, Delis DC, Bondi MW. Specific measures of executive function predict cognitive decline in older adults. J Int Neuropsychol Soc 2012;18:118-127.
crossref pmid pmc
6. Chang JS, Chi YK, Han JW, Kim TH, Youn JC, Lee SB, et al. Altered categorization of semantic knowledge in Korean patients with Alzheimer’s disease. J Alzheimers Dis 2013;36:41-48.
crossref pmid
7. Binder JR, Desai RH. The neurobiology of semantic memory. Trends Cogn Sci 2011;15:527-536.
crossref pmid pmc
8. Meinzer M, Flaisch T, Wilser L, Eulitz C, Rockstroh B, Conway T, et al. Neural signatures of semantic and phonemic fluency in young and old adults. J Cogn Neurosci 2009;21:2007-2018.
crossref pmid pmc pdf
9. Wagner S, Sebastian A, Lieb K, Tüscher O, Tadić A. A coordinate-based ALE functional MRI meta-analysis of brain activation during verbal fluency tasks in healthy control subjects. BMC Neurosci 2014;15:19
crossref pmid pmc pdf
10. Ishkhanyan B, Michel Lange V, Boye K, Mogensen J, Karabanov A, Hartwigsen G, et al. Anterior and posterior left inferior frontal gyrus contribute to the implementation of grammatical determiners during language production. Front Psychol 2020;11:685
crossref pmid pmc
11. Birn RM, Kenworthy L, Case L, Caravella R, Jones TB, Bandettini PA, et al. Neural systems supporting lexical search guided by letter and semantic category cues: a self-paced overt response fMRI study of verbal fluency. NeuroImage 2010;49:1099-1107.
crossref pmid pmc
12. Henry JD, Crawford JR. A meta-analytic review of verbal fluency performance in patients with traumatic brain injury. Neuropsychology 2004;18:621-628.
crossref pmid
13. Schmidt CSM, Nitschke K, Bormann T, Römer P, Kümmerer D, Martin M, et al. Dissociating frontal and temporal correlates of phonological and semantic fluency in a large sample of left hemisphere stroke patients. Neuroimage Clin 2019;23:101840
crossref pmid pmc
14. Biesbroek JM, van Zandvoort MJ, Kappelle LJ, Velthuis BK, Biessels GJ, Postma A. Shared and distinct anatomical correlates of semantic and phonemic fluency revealed by lesion-symptom mapping in patients with ischemic stroke. Brain Struct Funct 2016;221:2123-2134.
crossref pmid pmc pdf
15. Ahn HJ, Seo SW, Chin J, Suh MK, Lee BH, Kim ST, et al. The cortical neuroanatomy of neuropsychological deficits in mild cognitive impairment and Alzheimer’s disease: a surface-based morphometric analysis. Neuropsychologia 2011;49:3931-3945.
crossref pmid
16. Vonk JMJ, Rizvi B, Lao PJ, Budge M, Manly JJ, Mayeux R, et al. Letter and category fluency performance correlates with distinct patterns of cortical thickness in older adults. Cereb Cortex 2019;29:2694-2700.
crossref pmid pmc
17. Apostolova LG, Lu P, Rogers S, Dutton RA, Hayashi KM, Toga AW, et al. 3D mapping of language networks in clinical and pre-clinical Alzheimer’s disease. Brain Lang 2008;104:33-41.
crossref pmid pmc
18. Yap KH, Ung WC, Ebenezer EGM, Nordin N, Chin PS, Sugathan S, et al. Visualizing hyperactivation in neurodegeneration based on prefrontal oxygenation: a comparative study of mild Alzheimer’s disease, mild cognitive impairment, and healthy controls. Front Aging Neurosci 2017;9:287
crossref pmid pmc
19. Yeung MK, Sze SL, Woo J, Kwok T, Shum DH, Yu R, et al. Altered frontal lateralization underlies the category fluency deficits in older adults with mild cognitive impairment: a near-infrared spectroscopy study. Front Aging Neurosci 2016;8:59
crossref pmid pmc
20. Herrmann MJ, Langer JB, Jacob C, Ehlis AC, Fallgatter AJ. Reduced prefrontal oxygenation in Alzheimer disease during verbal fluency tasks. Am J Geriatr Psychiatry 2008;16:125-135.
crossref pmid
21. Katzorke A, Zeller JBM, Müller LD, Lauer M, Polak T, Deckert J, et al. Decreased hemodynamic response in inferior frontotemporal regions in elderly with mild cognitive impairment. Psychiatry Res Neuroimaging 2018;274:11-18.
crossref pmid
22. Metzger FG, Schopp B, Haeussinger FB, Dehnen K, Synofzik M, Fallgatter AJ, et al. Brain activation in frontotemporal and Alzheimer’s dementia: a functional near-infrared spectroscopy study. Alzheimers Res Ther 2016;8:56
crossref pmid pmc pdf
23. Yang D, Hong KS, Yoo SH, Kim CS. Evaluation of neural degeneration biomarkers in the prefrontal cortex for early identification of patients with mild cognitive impairment: an fNIRS study. Front Hum Neurosci 2019;13:317
crossref pmid pmc
24. Cabeza R, Anderson ND, Locantore JK, McIntosh AR. Aging gracefully: compensatory brain activity in high-performing older adults. Neuroimage 2002;17:1394-1402.
crossref pmid
25. Rosselli M, Ardila A, Matute E, Vélez-Uribe I. Language development across the life span: a neuropsychological/neuroimaging perspective. Neurosci J 2014;2014:585237
crossref pmid pmc pdf
26. Clement F, Belleville S. Effect of disease severity on neural compensation of item and associative recognition in mild cognitive impairment. J Alzheimers Dis 2012;29:109-123.
crossref pmid
27. Price CJ, Friston KJ. Degeneracy and cognitive anatomy. Trends Cogn Sci 2002;6:416-421.
crossref pmid
28. Cabeza R. Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychol Aging 2002;17:85-100.
crossref pmid
29. Han JW, Kim TH, Kwak KP, Kim K, Kim BJ, Kim SG, et al. Overview of the Korean longitudinal study on cognitive aging and dementia. Psychiatry investig 2018;15:767-774.
crossref pmid pmc pdf
30. Lee JH, Lee KU, Lee DY, Kim KW, Jhoo JH, Kim JH, et al. Development of the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease Assessment Packet (CERAD-K): clinical and neuropsychological assessment batteries. J Gerontol B Psychol Sci Soc Sci 2002;57:P47-P53.
crossref pmid
31. Yoo SW, Kim YS, Noh JS, Oh KS, Kim CH, Namkoong K, et al. Validity of Korean version of the mini-international neuropsychiatric interview. Anxiety Mood 2006;2:50-55.

32. Han JY, Seo EH, Yi D, Sohn BK, Choe YM, Byun MS, et al. A normative study of total scores of the CERAD neuropsychological assessment battery in an educationally diverse elderly population. Int Psychogeriatr 2014;26:1897-1904.
crossref pmid
33. Seo EH, Lee DY, Lee JH, Choo IH, Kim JW, Kim SG, et al. Total scores of the CERAD neuropsychological assessment battery: validation for mild cognitive impairment and dementia patients with diverse etiologies. Am J Geriatr Psychiatry 2010;18:801-809.
crossref pmid
34. Lee DY, Lee KU, Lee JH, Kim KW, Jhoo JH, Kim SY, et al. A normative study of the CERAD neuropsychological assessment battery in the Korean elderly. J Int Neuropsychol Soc 2004;10:72-81.
crossref pmid
35. Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993;43:2412-2414.
crossref
36. Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund LO, et al. Mild cognitive impairment-beyond controversies, towards a consensus: report of the international working group on mild cognitive impairment. J Intern Med 2004;256:240-246.
crossref pmid
37. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006;31:968-980.
crossref pmid
38. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cerebral Cortex 2004;14:11-22.
crossref pmid
39. Klein A, Tourville J. 101 labeled brain images and a consistent human cortical labeling protocol. Front Neurosci 2012;6:171
crossref pmid pmc
40. Fischl B. FreeSurfer. Neuroimage 2012;62:774-781.
crossref pmid pmc
41. Forbes SH, Wijeakumar S, Eggebrecht AT, Magnotta VA, Spencer JP. Processing pipeline for image reconstructed fNIRS analysis using both MRI templates and individual anatomy. Neurophotonics 2021;8:025010
crossref pmid pmc
42. Aasted CM, Yücel MA, Cooper RJ, Dubb J, Tsuzuki D, Becerra L, et al. Anatomical guidance for functional near-infrared spectroscopy: AtlasViewer tutorial. Neurophotonics 2015;2:020801
crossref pmid pmc
43. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002;15:273-289.
crossref pmid
44. Sassaroli A, Fantini S. Comment on the modified Beer-Lambert law for scattering media. Phys Med Biol 2004;49:N255-N257.
crossref pmid
45. Naseer N, Hong KS. fNIRS-based brain-computer interfaces: a review. Front Hum Neurosci 2015;9:3
crossref pmid pmc
46. Khan MJ, Hong KS. Hybrid EEG-fNIRS-based eight-command decoding for BCI: application to quadcopter control. Front Neurorobot 2017;11:6
crossref pmid pmc
47. Abdelnour F, Schmidt B, Huppert TJ. Topographic localization of brain activation in diffuse optical imaging using spherical wavelets. Phys Med Biol 2009;54:6383-6413.
crossref pmid pmc
48. Leff DR, Orihuela-Espina F, Elwell CE, Athanasiou T, Delpy DT, Darzi AW, et al. Assessment of the cerebral cortex during motor task behaviours in adults: a systematic review of functional near infrared spectroscopy (fNIRS) studies. Neuroimage 2011;54:2922-2936.
crossref pmid
49. Strangman G, Culver JP, Thompson JH, Boas DA. A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation. Neuroimage 2002;17:719-731.
crossref pmid
50. Cui X, Bray S, Bryant DM, Glover GH, Reiss AL. A quantitative comparison of NIRS and fMRI across multiple cognitive tasks. NeuroImage 2011;54:2808-2821.
crossref pmid pmc
51. Yang M, Yang Z, Yuan T, Feng W, Wang P. A systemic review of functional near-infrared spectroscopy for stroke: current application and future directions. Front Neurol 2019;10:58
crossref pmid pmc
52. Kirilina E, Yu N, Jelzow A, Wabnitz H, Jacobs AM, Tachtsidis I. Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex. Front Hum Neurosci 2013;7:864
pmid pmc
53. Dans PW, Foglia SD, Nelson AJ. Data processing in functional near-infrared spectroscopy (fNIRS) motor control research. Brain Sci 2021;11:606
crossref pmid pmc
54. Yennu A, Tian F, Smith-Osborne A, J Gatchel R, Woon FL, Liu H. Prefrontal responses to Stroop tasks in subjects with post-traumatic stress disorder assessed by functional near infrared spectroscopy. Sci Rep 2016;6:30157
crossref pmid pmc pdf
55. von Lühmann A, Ortega-Martinez A, Boas DA, Yücel MA. Using the general linear model to improve performance in fNIRS single trial analysis and classification: a perspective. Front Hum Neurosci 2020;14:30
pmid pmc
56. Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, et al. MEG and EEG data analysis with MNE-Python. Front Neurosci 2013;7:267
crossref pmid pmc
57. Mirelman A, Maidan I, Bernad-Elazari H, Nieuwhof F, Reelick M, Giladi N, et al. Increased frontal brain activation during walking while dual tasking: an fNIRS study in healthy young adults. J Neuroeng Rehabil 2014;11:85
crossref pmid pmc
58. Holtzer R, Izzetoglu M. Mild cognitive impairments attenuate prefrontal cortex activations during walking in older adults. Brain Sci 2020;10:415
crossref pmid pmc
59. Wagshul ME, Lucas M, Ye K, Izzetoglu M, Holtzer R. Multi-modal neuroimaging of dual-task walking: structural MRI and fNIRS analysis reveals prefrontal grey matter volume moderation of brain activation in older adults. NeuroImage 2019;189:745-754.
crossref pmid pmc
60. Herrmann MJ, Walter A, Ehlis AC, Fallgatter AJ. Cerebral oxygenation changes in the prefrontal cortex: effects of age and gender. Neurobiol Aging 2006;27:888-894.
crossref pmid
61. Heinzel S, Metzger FG, Ehlis AC, Korell R, Alboji A, Haeussinger FB, et al. Aging-related cortical reorganization of verbal fluency processing: a functional near-infrared spectroscopy study. Neurobiol Aging 2013;34:439-450.
crossref pmid
62. Katzev M, Tüscher O, Hennig J, Weiller C, Kaller CP. Revisiting the functional specialization of left inferior frontal gyrus in phonological and semantic fluency: the crucial role of task demands and individual ability. J Neurosci 2013;33:7837-7845.
crossref pmid pmc
63. Elmer S. Broca pars triangularis constitutes a “Hub” of the language-control network during simultaneous language translation. Front Hum Neurosci 2016;10:491
crossref pmid pmc
64. Park DC, Reuter-Lorenz P. The adaptive brain: aging and neurocognitive scaffolding. Annu Rev Psychol 2009;60:173-196.
crossref pmid pmc
65. Eastman JA, Hwang KS, Lazaris A, Chow N, Ramirez L, Babakchanian S, et al. Cortical thickness and semantic fluency in Alzheimer’s disease and mild cognitive impairment. Am J Alzheimers Dis (Columbia) 2013;1:81-92.
crossref pmid pmc
66. Preibisch C, Haase A. Perfusion imaging using spin-labeling methods: contrast-to-noise comparison in functional MRI applications. Magn Reson Med 2001;46:172-182.
crossref pmid
67. Gawryluk JR, Mazerolle EL, D’Arcy RC. Does functional MRI detect activation in white matter? A review of emerging evidence, issues, and future directions. Front Neurosci 2014;8:239
crossref pmid pmc
68. Helenius J, Perkiö J, Soinne L, Østergaard L, Carano RA, Salonen O, et al. Cerebral hemodynamics in a healthy population measured by dynamic susceptibility contrast MR imaging. Acta Radiol 2003;44:538-546.
crossref pmid pdf


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